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b3413a70bbc5e925ec9a604338eb4ffab031b9a0 | Zaratahir123/test | [
"license:mit",
"region:us"
] | 2022-04-27T09:45:17+00:00 | {"license": "mit"} | 2022-04-27T09:45:17+00:00 |
|
d3f7c9ee72dd91ddcb840a8585d248deffa0e5a0 | shreyasmani/whrdata2021 | [
"license:other",
"region:us"
] | 2022-04-27T10:10:04+00:00 | {"license": "other"} | 2022-04-27T10:11:59+00:00 |
|
96a234bd25c04939c4a79213eb764ae90e4d0d81 |
# Dataset Card for BANKING77
## 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:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
- **Repository:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
- **Paper:** [ArXiv](https://arxiv.org/abs/2003.04807)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Dataset composed of online banking queries annotated with their corresponding intents.
BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
It comprises 13,083 customer service queries labeled with 77 intents.
It focuses on fine-grained single-domain intent detection.
### Supported Tasks and Leaderboards
Intent classification, intent detection
### Languages
English
## Dataset Structure
### Data Instances
An example of 'train' looks as follows:
```
{
'label': 11, # integer label corresponding to "card_arrival" intent
'text': 'I am still waiting on my card?'
}
```
### Data Fields
- `text`: a string feature.
- `label`: One of classification labels (0-76) corresponding to unique intents.
Intent names are mapped to `label` in the following way:
| label | intent (category) |
|---:|:-------------------------------------------------|
| 0 | activate_my_card |
| 1 | age_limit |
| 2 | apple_pay_or_google_pay |
| 3 | atm_support |
| 4 | automatic_top_up |
| 5 | balance_not_updated_after_bank_transfer |
| 6 | balance_not_updated_after_cheque_or_cash_deposit |
| 7 | beneficiary_not_allowed |
| 8 | cancel_transfer |
| 9 | card_about_to_expire |
| 10 | card_acceptance |
| 11 | card_arrival |
| 12 | card_delivery_estimate |
| 13 | card_linking |
| 14 | card_not_working |
| 15 | card_payment_fee_charged |
| 16 | card_payment_not_recognised |
| 17 | card_payment_wrong_exchange_rate |
| 18 | card_swallowed |
| 19 | cash_withdrawal_charge |
| 20 | cash_withdrawal_not_recognised |
| 21 | change_pin |
| 22 | compromised_card |
| 23 | contactless_not_working |
| 24 | country_support |
| 25 | declined_card_payment |
| 26 | declined_cash_withdrawal |
| 27 | declined_transfer |
| 28 | direct_debit_payment_not_recognised |
| 29 | disposable_card_limits |
| 30 | edit_personal_details |
| 31 | exchange_charge |
| 32 | exchange_rate |
| 33 | exchange_via_app |
| 34 | extra_charge_on_statement |
| 35 | failed_transfer |
| 36 | fiat_currency_support |
| 37 | get_disposable_virtual_card |
| 38 | get_physical_card |
| 39 | getting_spare_card |
| 40 | getting_virtual_card |
| 41 | lost_or_stolen_card |
| 42 | lost_or_stolen_phone |
| 43 | order_physical_card |
| 44 | passcode_forgotten |
| 45 | pending_card_payment |
| 46 | pending_cash_withdrawal |
| 47 | pending_top_up |
| 48 | pending_transfer |
| 49 | pin_blocked |
| 50 | receiving_money |
| 51 | Refund_not_showing_up |
| 52 | request_refund |
| 53 | reverted_card_payment? |
| 54 | supported_cards_and_currencies |
| 55 | terminate_account |
| 56 | top_up_by_bank_transfer_charge |
| 57 | top_up_by_card_charge |
| 58 | top_up_by_cash_or_cheque |
| 59 | top_up_failed |
| 60 | top_up_limits |
| 61 | top_up_reverted |
| 62 | topping_up_by_card |
| 63 | transaction_charged_twice |
| 64 | transfer_fee_charged |
| 65 | transfer_into_account |
| 66 | transfer_not_received_by_recipient |
| 67 | transfer_timing |
| 68 | unable_to_verify_identity |
| 69 | verify_my_identity |
| 70 | verify_source_of_funds |
| 71 | verify_top_up |
| 72 | virtual_card_not_working |
| 73 | visa_or_mastercard |
| 74 | why_verify_identity |
| 75 | wrong_amount_of_cash_received |
| 76 | wrong_exchange_rate_for_cash_withdrawal |
### Data Splits
| Dataset statistics | Train | Test |
| --- | --- | --- |
| Number of examples | 10 003 | 3 080 |
| Average character length | 59.5 | 54.2 |
| Number of intents | 77 | 77 |
| Number of domains | 1 | 1 |
## Dataset Creation
### Curation Rationale
Previous intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale *multi-domain* datasets ([HWU64](https://github.com/xliuhw/NLU-Evaluation-Data) and [CLINC150](https://github.com/clinc/oos-eval)), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered "in the wild". This dataset tries to fill the gap and provides a very fine-grained set of intents in a *single-domain* i.e. **banking**. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The dataset does not contain any additional annotations.
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset it to help develop better intent detection systems.
Any comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[PolyAI](https://github.com/PolyAI-LDN)
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
```
@inproceedings{Casanueva2020,
author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
title = {Efficient Intent Detection with Dual Sentence Encoders},
year = {2020},
month = {mar},
note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets},
url = {https://arxiv.org/abs/2003.04807},
booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020}
}
```
### Contributions
Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset.
| PolyAI/banking77 | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2003.04807",
"region:us"
] | 2022-04-27T11:54:13+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["intent-classification", "multi-class-classification"], "pretty_name": "BANKING77", "extended": ["original"]} | 2022-10-25T09:12:22+00:00 |
e504e76976d00dbe5d2ee3945bf6d42c65e2bd1d | # AutoTrain Dataset for project: Rule
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project Rule.
### Languages
The BCP-47 code for the dataset's language is zh.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u672c\u516c\u53f8\u4f1a\u5728\u60a8\u767b\u5f55\u53ca\u7248\u672c\u66f4\u65b0\u65f6\u4ee5\u63a8\u9001\u901a\u77e5\u3001\u5f39\u6846\u7684\u5f62\u5f0f\u5411\u60a8\u5c55\u793a\u53d8\u66f4\u540e\u7684\u9690\u79c1\u653f\u7b56",
"target": 1
},
{
"text": "\u6211\u4eec\u53ef\u80fd\u9002\u65f6\u4f1a\u5bf9\u672c\u9690\u79c1\u6743\u653f\u7b56\u8fdb\u884c\u8c03\u6574\u6216\u53d8\u66f4\uff0c\u672c\u9690\u79c1\u6743\u653f\u7b56\u7684\u4efb\u4f55\u66f4\u65b0\u5c06\u4ee5\u6807\u6ce8\u66f4\u65b0\u65f6\u95f4\u7684\u65b9\u5f0f\u516c\u5e03\u5728\u6211\u4eec\u7f51\u7ad9\u4e0a\uff0c\u9664\u6cd5\u5f8b\u6cd5\u89c4\u6216\u76d1\u7ba1\u89c4\u5b9a\u53e6\u6709\u5f3a\u5236\u6027\u89c4\u5b9a\u5916\uff0c\u7ecf\u8c03\u6574\u6216\u53d8\u66f4\u7684\u5185\u5bb9\u4e00\u7ecf\u901a\u77e5\u6216\u516c\u5e03\u540e\u76847\u65e5\u540e\u751f\u6548",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['0', '1'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 70 |
| valid | 19 |
| EAST/autotrain-data-Rule | [
"task_categories:text-classification",
"language:zh",
"region:us"
] | 2022-04-27T13:55:32+00:00 | {"language": ["zh"], "task_categories": ["text-classification"]} | 2022-10-25T09:12:41+00:00 |
904009d6f19d0d4eabb8f1471a92d49c839920d5 |
This dataset contains sentence-level formality annotations used in the 2016
TACL paper "An Empirical Analysis of Formality in Online Communication"
(Pavlick and Tetreault, 2016). It includes sentences from four genres (news,
blogs, email, and QA forums), all annotated by humans on Amazon Mechanical
Turk. The news and blog data was collected by Shibamouli Lahiri, and we are
redistributing it here for the convenience of other researchers. We collected
the email and answers data ourselves, using a similar annotation setup to
Shibamouli.
In the original dataset, `answers` and `email` were tokenized. In this version,
Oleksiy Syvokon detokenized them with `moses-detokenizer` and a bunch of
additional regexps.
If you use this data in your work, please cite BOTH of the below papers:
```
@article{PavlickAndTetreault-2016:TACL,
author = {Ellie Pavlick and Joel Tetreault},
title = {An Empirical Analysis of Formality in Online Communication},
journal = {Transactions of the Association for Computational Linguistics},
year = {2016},
publisher = {Association for Computational Linguistics}
}
@article{Lahiri-2015:arXiv,
title={{SQUINKY! A} Corpus of Sentence-level Formality, Informativeness, and Implicature},
author={Lahiri, Shibamouli},
journal={arXiv preprint arXiv:1506.02306},
year={2015}
}
```
## Contents
The annotated data files and number of lines in each are as follows:
* 4977 answers -- Annotated sentences from a random sample of posts from the Yahoo! Answers forums: https://answers.yahoo.com/
* 1821 blog -- Annotated sentences from the top 100 blogs listed on http://technorati.com/ on October 31, 2009.
* 1701 email -- Annotated sentences from a random sample of emails from the Jeb Bush email archive: http://americanbridgepac.org/jeb-bushs-gubernatorial-email-archive/
* 2775 news -- Annotated sentences from the "breaking", "recent", and "local" news sections of the following 20 news sites: CNN, CBS News, ABC News, Reuters, BBC News Online, New York Times, Los Angeles Times, The Guardian (U.K.), Voice of America, Boston Globe, Chicago Tribune, San Francisco Chronicle, Times Online (U.K.), news.com.au, Xinhua, The Times of India, Seattle Post Intelligencer, Daily Mail, and Bloomberg L.P.
## Format
Each record contains the following fields:
1. `avg_score`: the mean formality rating, which ranges from -3 to 3 where lower scores indicate less formal sentences
2. `sentence`
| osyvokon/pavlick-formality-scores | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | 2022-04-27T14:28:07+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["text-scoring"], "pretty_name": "Sentence-level formality annotations for news, blogs, email and QA forums.\n\nPublished in \"An Empirical Analysis of Formality in Online Communication\" (Pavlick and Tetreault, 2016) "} | 2023-11-24T16:41:26+00:00 |
2d04a4fde26d92fbed2ae7604677736e6b901a4d | This file contains news texts (sentences) belonging to different writing styles. The original dataset created by {*Upeksha, D., Wijayarathna, C., Siriwardena, M.,
Lasandun, L., Wimalasuriya, C., de Silva, N., and Dias, G. (2015). Implementing a corpus for Sinhala language. 01*}is processed and cleaned.
If you use this dataset, please cite {*Dhananjaya et al. BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, 2022*} and the above mentioned paper. | NLPC-UOM/Writing-style-classification | [
"task_categories:text-classification",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:si",
"license:mit",
"region:us"
] | 2022-04-27T17:08:07+00:00 | {"annotations_creators": [], "language_creators": ["crowdsourced"], "language": ["si"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "sinhala-writing-style-classification"} | 2022-10-25T09:12:46+00:00 |
e220d18727665580a083db6162dd78f9e0f61438 |
Original from TODO.
# Data Statement for SHADES
> **How to use this document:**
> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.
> For full details, the best source is the original Data Statements paper, here: https://www.aclweb.org/anthology/Q18-1041/ .
> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as "DATASTATEMENT.md". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.
> Only blockquoted content should be deleted; the final about statement should be left intact.
Data set name: Bias-Shades
Citation (if available): TODO.
Data set developer(s): TODO.
Data statement author(s): TODO.
Others who contributed to this document: N/A
License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).
## A. CURATION RATIONALE
> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.
This dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted.
<!-- We then adapted the crowdsourcing method described by [Nangia et al. (2020)](https://arxiv.org/pdf/2010.00133) to collect additional sentences expressing a stereotype relevant to the French socio-cultural environment. Data collection is implemented through LanguageARC [(Fiumara et al., 2020)](https://www.aclweb.org/anthology/2020.cllrd-1.1.pdf), a citizen science platform supporting the development of language resources dedicated to social improvement. We created a LanguageARC project (https://languagearc.com/projects/19) to collect these additional sentences. Participants were asked to submit a statement that expressed a stereotype in French along with a selection of ten bias types: the nine bias types offered in CrowS-Pairs and the additional category _other_. We collected 210 additional sentences this way. -->
## B. LANGUAGE VARIETY/VARIETIES
> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., "English as spoken in Palo Alto, California", or "Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin").
* BCP-47 language tags: en-US, fr-FR, ....
* Language variety description: English spoken by native American speakers, native French people from metropolitan France.
## C. CONTRIBUTOR DEMOGRAPHIC
> ## C. SPEAKER DEMOGRAPHIC
> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include:
N/A
## D. ANNOTATOR DEMOGRAPHIC
> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:
Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group.
## E. SPEECH SITUATION
N/A
## F. TEXT CHARACTERISTICS
> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.
Collected data is a collection of offensive stereotyped statements in French, they might be upsetting.
Along these stereotyped statements are paired anti-stereotyped statements.
## G. RECORDING QUALITY
N/A
## H. OTHER
> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.
## I. PROVENANCE APPENDIX
Examples were gathered using the LanguageArc site and by creating a dedicated project: https://languagearc.com/projects/19
## About this document
A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.
Data Statements are from the University of Washington. Contact: [datastatements@uw.edu](mailto:datastatements@uw.edu). This document template is licensed as [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
This version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the [2020 LREC workshop on Data Statements](https://sites.google.com/uw.edu/data-statements-for-nlp/), by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski. | bigscience-catalogue-data/bias-shades | [
"license:cc-by-sa-4.0",
"arxiv:2010.00133",
"region:us"
] | 2022-04-27T17:49:56+00:00 | {"license": "cc-by-sa-4.0"} | 2022-05-01T14:18:02+00:00 |
f004a913f9170e5ff39e63f6172ee3ae918197c9 | mapping:
```
n01440764 tench, Tinca tinca
n01443537 goldfish, Carassius auratus
n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
n01491361 tiger shark, Galeocerdo cuvieri
n01494475 hammerhead, hammerhead shark
n01496331 electric ray, crampfish, numbfish, torpedo
n01498041 stingray
n01514668 cock
n01514859 hen
n01518878 ostrich, Struthio camelus
n01530575 brambling, Fringilla montifringilla
n01531178 goldfinch, Carduelis carduelis
n01532829 house finch, linnet, Carpodacus mexicanus
n01534433 junco, snowbird
n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea
n01558993 robin, American robin, Turdus migratorius
n01560419 bulbul
n01580077 jay
n01582220 magpie
n01592084 chickadee
n01601694 water ouzel, dipper
n01608432 kite
n01614925 bald eagle, American eagle, Haliaeetus leucocephalus
n01616318 vulture
n01622779 great grey owl, great gray owl, Strix nebulosa
n01629819 European fire salamander, Salamandra salamandra
n01630670 common newt, Triturus vulgaris
n01631663 eft
n01632458 spotted salamander, Ambystoma maculatum
n01632777 axolotl, mud puppy, Ambystoma mexicanum
n01641577 bullfrog, Rana catesbeiana
n01644373 tree frog, tree-frog
n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
n01664065 loggerhead, loggerhead turtle, Caretta caretta
n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
n01667114 mud turtle
n01667778 terrapin
n01669191 box turtle, box tortoise
n01675722 banded gecko
n01677366 common iguana, iguana, Iguana iguana
n01682714 American chameleon, anole, Anolis carolinensis
n01685808 whiptail, whiptail lizard
n01687978 agama
n01688243 frilled lizard, Chlamydosaurus kingi
n01689811 alligator lizard
n01692333 Gila monster, Heloderma suspectum
n01693334 green lizard, Lacerta viridis
n01694178 African chameleon, Chamaeleo chamaeleon
n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
n01697457 African crocodile, Nile crocodile, Crocodylus niloticus
n01698640 American alligator, Alligator mississipiensis
n01704323 triceratops
n01728572 thunder snake, worm snake, Carphophis amoenus
n01728920 ringneck snake, ring-necked snake, ring snake
n01729322 hognose snake, puff adder, sand viper
n01729977 green snake, grass snake
n01734418 king snake, kingsnake
n01735189 garter snake, grass snake
n01737021 water snake
n01739381 vine snake
n01740131 night snake, Hypsiglena torquata
n01742172 boa constrictor, Constrictor constrictor
n01744401 rock python, rock snake, Python sebae
n01748264 Indian cobra, Naja naja
n01749939 green mamba
n01751748 sea snake
n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus
n01756291 sidewinder, horned rattlesnake, Crotalus cerastes
n01768244 trilobite
n01770081 harvestman, daddy longlegs, Phalangium opilio
n01770393 scorpion
n01773157 black and gold garden spider, Argiope aurantia
n01773549 barn spider, Araneus cavaticus
n01773797 garden spider, Aranea diademata
n01774384 black widow, Latrodectus mactans
n01774750 tarantula
n01775062 wolf spider, hunting spider
n01776313 tick
n01784675 centipede
n01795545 black grouse
n01796340 ptarmigan
n01797886 ruffed grouse, partridge, Bonasa umbellus
n01798484 prairie chicken, prairie grouse, prairie fowl
n01806143 peacock
n01806567 quail
n01807496 partridge
n01817953 African grey, African gray, Psittacus erithacus
n01818515 macaw
n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
n01820546 lorikeet
n01824575 coucal
n01828970 bee eater
n01829413 hornbill
n01833805 hummingbird
n01843065 jacamar
n01843383 toucan
n01847000 drake
n01855032 red-breasted merganser, Mergus serrator
n01855672 goose
n01860187 black swan, Cygnus atratus
n01871265 tusker
n01872401 echidna, spiny anteater, anteater
n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
n01877812 wallaby, brush kangaroo
n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
n01883070 wombat
n01910747 jellyfish
n01914609 sea anemone, anemone
n01917289 brain coral
n01924916 flatworm, platyhelminth
n01930112 nematode, nematode worm, roundworm
n01943899 conch
n01944390 snail
n01945685 slug
n01950731 sea slug, nudibranch
n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore
n01968897 chambered nautilus, pearly nautilus, nautilus
n01978287 Dungeness crab, Cancer magister
n01978455 rock crab, Cancer irroratus
n01980166 fiddler crab
n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus
n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
n01985128 crayfish, crawfish, crawdad, crawdaddy
n01986214 hermit crab
n01990800 isopod
n02002556 white stork, Ciconia ciconia
n02002724 black stork, Ciconia nigra
n02006656 spoonbill
n02007558 flamingo
n02009229 little blue heron, Egretta caerulea
n02009912 American egret, great white heron, Egretta albus
n02011460 bittern
n02012849 crane
n02013706 limpkin, Aramus pictus
n02017213 European gallinule, Porphyrio porphyrio
n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana
n02018795 bustard
n02025239 ruddy turnstone, Arenaria interpres
n02027492 red-backed sandpiper, dunlin, Erolia alpina
n02028035 redshank, Tringa totanus
n02033041 dowitcher
n02037110 oystercatcher, oyster catcher
n02051845 pelican
n02056570 king penguin, Aptenodytes patagonica
n02058221 albatross, mollymawk
n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca
n02074367 dugong, Dugong dugon
n02077923 sea lion
n02085620 Chihuahua
n02085782 Japanese spaniel
n02085936 Maltese dog, Maltese terrier, Maltese
n02086079 Pekinese, Pekingese, Peke
n02086240 Shih-Tzu
n02086646 Blenheim spaniel
n02086910 papillon
n02087046 toy terrier
n02087394 Rhodesian ridgeback
n02088094 Afghan hound, Afghan
n02088238 basset, basset hound
n02088364 beagle
n02088466 bloodhound, sleuthhound
n02088632 bluetick
n02089078 black-and-tan coonhound
n02089867 Walker hound, Walker foxhound
n02089973 English foxhound
n02090379 redbone
n02090622 borzoi, Russian wolfhound
n02090721 Irish wolfhound
n02091032 Italian greyhound
n02091134 whippet
n02091244 Ibizan hound, Ibizan Podenco
n02091467 Norwegian elkhound, elkhound
n02091635 otterhound, otter hound
n02091831 Saluki, gazelle hound
n02092002 Scottish deerhound, deerhound
n02092339 Weimaraner
n02093256 Staffordshire bullterrier, Staffordshire bull terrier
n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
n02093647 Bedlington terrier
n02093754 Border terrier
n02093859 Kerry blue terrier
n02093991 Irish terrier
n02094114 Norfolk terrier
n02094258 Norwich terrier
n02094433 Yorkshire terrier
n02095314 wire-haired fox terrier
n02095570 Lakeland terrier
n02095889 Sealyham terrier, Sealyham
n02096051 Airedale, Airedale terrier
n02096177 cairn, cairn terrier
n02096294 Australian terrier
n02096437 Dandie Dinmont, Dandie Dinmont terrier
n02096585 Boston bull, Boston terrier
n02097047 miniature schnauzer
n02097130 giant schnauzer
n02097209 standard schnauzer
n02097298 Scotch terrier, Scottish terrier, Scottie
n02097474 Tibetan terrier, chrysanthemum dog
n02097658 silky terrier, Sydney silky
n02098105 soft-coated wheaten terrier
n02098286 West Highland white terrier
n02098413 Lhasa, Lhasa apso
n02099267 flat-coated retriever
n02099429 curly-coated retriever
n02099601 golden retriever
n02099712 Labrador retriever
n02099849 Chesapeake Bay retriever
n02100236 German short-haired pointer
n02100583 vizsla, Hungarian pointer
n02100735 English setter
n02100877 Irish setter, red setter
n02101006 Gordon setter
n02101388 Brittany spaniel
n02101556 clumber, clumber spaniel
n02102040 English springer, English springer spaniel
n02102177 Welsh springer spaniel
n02102318 cocker spaniel, English cocker spaniel, cocker
n02102480 Sussex spaniel
n02102973 Irish water spaniel
n02104029 kuvasz
n02104365 schipperke
n02105056 groenendael
n02105162 malinois
n02105251 briard
n02105412 kelpie
n02105505 komondor
n02105641 Old English sheepdog, bobtail
n02105855 Shetland sheepdog, Shetland sheep dog, Shetland
n02106030 collie
n02106166 Border collie
n02106382 Bouvier des Flandres, Bouviers des Flandres
n02106550 Rottweiler
n02106662 German shepherd, German shepherd dog, German police dog, alsatian
n02107142 Doberman, Doberman pinscher
n02107312 miniature pinscher
n02107574 Greater Swiss Mountain dog
n02107683 Bernese mountain dog
n02107908 Appenzeller
n02108000 EntleBucher
n02108089 boxer
n02108422 bull mastiff
n02108551 Tibetan mastiff
n02108915 French bulldog
n02109047 Great Dane
n02109525 Saint Bernard, St Bernard
n02109961 Eskimo dog, husky
n02110063 malamute, malemute, Alaskan malamute
n02110185 Siberian husky
n02110341 dalmatian, coach dog, carriage dog
n02110627 affenpinscher, monkey pinscher, monkey dog
n02110806 basenji
n02110958 pug, pug-dog
n02111129 Leonberg
n02111277 Newfoundland, Newfoundland dog
n02111500 Great Pyrenees
n02111889 Samoyed, Samoyede
n02112018 Pomeranian
n02112137 chow, chow chow
n02112350 keeshond
n02112706 Brabancon griffon
n02113023 Pembroke, Pembroke Welsh corgi
n02113186 Cardigan, Cardigan Welsh corgi
n02113624 toy poodle
n02113712 miniature poodle
n02113799 standard poodle
n02113978 Mexican hairless
n02114367 timber wolf, grey wolf, gray wolf, Canis lupus
n02114548 white wolf, Arctic wolf, Canis lupus tundrarum
n02114712 red wolf, maned wolf, Canis rufus, Canis niger
n02114855 coyote, prairie wolf, brush wolf, Canis latrans
n02115641 dingo, warrigal, warragal, Canis dingo
n02115913 dhole, Cuon alpinus
n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
n02117135 hyena, hyaena
n02119022 red fox, Vulpes vulpes
n02119789 kit fox, Vulpes macrotis
n02120079 Arctic fox, white fox, Alopex lagopus
n02120505 grey fox, gray fox, Urocyon cinereoargenteus
n02123045 tabby, tabby cat
n02123159 tiger cat
n02123394 Persian cat
n02123597 Siamese cat, Siamese
n02124075 Egyptian cat
n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
n02127052 lynx, catamount
n02128385 leopard, Panthera pardus
n02128757 snow leopard, ounce, Panthera uncia
n02128925 jaguar, panther, Panthera onca, Felis onca
n02129165 lion, king of beasts, Panthera leo
n02129604 tiger, Panthera tigris
n02130308 cheetah, chetah, Acinonyx jubatus
n02132136 brown bear, bruin, Ursus arctos
n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus
n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
n02134418 sloth bear, Melursus ursinus, Ursus ursinus
n02137549 mongoose
n02138441 meerkat, mierkat
n02165105 tiger beetle
n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
n02167151 ground beetle, carabid beetle
n02168699 long-horned beetle, longicorn, longicorn beetle
n02169497 leaf beetle, chrysomelid
n02172182 dung beetle
n02174001 rhinoceros beetle
n02177972 weevil
n02190166 fly
n02206856 bee
n02219486 ant, emmet, pismire
n02226429 grasshopper, hopper
n02229544 cricket
n02231487 walking stick, walkingstick, stick insect
n02233338 cockroach, roach
n02236044 mantis, mantid
n02256656 cicada, cicala
n02259212 leafhopper
n02264363 lacewing, lacewing fly
n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
n02268853 damselfly
n02276258 admiral
n02277742 ringlet, ringlet butterfly
n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
n02280649 cabbage butterfly
n02281406 sulphur butterfly, sulfur butterfly
n02281787 lycaenid, lycaenid butterfly
n02317335 starfish, sea star
n02319095 sea urchin
n02321529 sea cucumber, holothurian
n02325366 wood rabbit, cottontail, cottontail rabbit
n02326432 hare
n02328150 Angora, Angora rabbit
n02342885 hamster
n02346627 porcupine, hedgehog
n02356798 fox squirrel, eastern fox squirrel, Sciurus niger
n02361337 marmot
n02363005 beaver
n02364673 guinea pig, Cavia cobaya
n02389026 sorrel
n02391049 zebra
n02395406 hog, pig, grunter, squealer, Sus scrofa
n02396427 wild boar, boar, Sus scrofa
n02397096 warthog
n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius
n02403003 ox
n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
n02410509 bison
n02412080 ram, tup
n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
n02417914 ibex, Capra ibex
n02422106 hartebeest
n02422699 impala, Aepyceros melampus
n02423022 gazelle
n02437312 Arabian camel, dromedary, Camelus dromedarius
n02437616 llama
n02441942 weasel
n02442845 mink
n02443114 polecat, fitch, foulmart, foumart, Mustela putorius
n02443484 black-footed ferret, ferret, Mustela nigripes
n02444819 otter
n02445715 skunk, polecat, wood pussy
n02447366 badger
n02454379 armadillo
n02457408 three-toed sloth, ai, Bradypus tridactylus
n02480495 orangutan, orang, orangutang, Pongo pygmaeus
n02480855 gorilla, Gorilla gorilla
n02481823 chimpanzee, chimp, Pan troglodytes
n02483362 gibbon, Hylobates lar
n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus
n02484975 guenon, guenon monkey
n02486261 patas, hussar monkey, Erythrocebus patas
n02486410 baboon
n02487347 macaque
n02488291 langur
n02488702 colobus, colobus monkey
n02489166 proboscis monkey, Nasalis larvatus
n02490219 marmoset
n02492035 capuchin, ringtail, Cebus capucinus
n02492660 howler monkey, howler
n02493509 titi, titi monkey
n02493793 spider monkey, Ateles geoffroyi
n02494079 squirrel monkey, Saimiri sciureus
n02497673 Madagascar cat, ring-tailed lemur, Lemur catta
n02500267 indri, indris, Indri indri, Indri brevicaudatus
n02504013 Indian elephant, Elephas maximus
n02504458 African elephant, Loxodonta africana
n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
n02514041 barracouta, snoek
n02526121 eel
n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
n02606052 rock beauty, Holocanthus tricolor
n02607072 anemone fish
n02640242 sturgeon
n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus
n02643566 lionfish
n02655020 puffer, pufferfish, blowfish, globefish
n02666196 abacus
n02667093 abaya
n02669723 academic gown, academic robe, judge's robe
n02672831 accordion, piano accordion, squeeze box
n02676566 acoustic guitar
n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier
n02690373 airliner
n02692877 airship, dirigible
n02699494 altar
n02701002 ambulance
n02704792 amphibian, amphibious vehicle
n02708093 analog clock
n02727426 apiary, bee house
n02730930 apron
n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
n02749479 assault rifle, assault gun
n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack
n02776631 bakery, bakeshop, bakehouse
n02777292 balance beam, beam
n02782093 balloon
n02783161 ballpoint, ballpoint pen, ballpen, Biro
n02786058 Band Aid
n02787622 banjo
n02788148 bannister, banister, balustrade, balusters, handrail
n02790996 barbell
n02791124 barber chair
n02791270 barbershop
n02793495 barn
n02794156 barometer
n02795169 barrel, cask
n02797295 barrow, garden cart, lawn cart, wheelbarrow
n02799071 baseball
n02802426 basketball
n02804414 bassinet
n02804610 bassoon
n02807133 bathing cap, swimming cap
n02808304 bath towel
n02808440 bathtub, bathing tub, bath, tub
n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
n02814860 beacon, lighthouse, beacon light, pharos
n02815834 beaker
n02817516 bearskin, busby, shako
n02823428 beer bottle
n02823750 beer glass
n02825657 bell cote, bell cot
n02834397 bib
n02835271 bicycle-built-for-two, tandem bicycle, tandem
n02837789 bikini, two-piece
n02840245 binder, ring-binder
n02841315 binoculars, field glasses, opera glasses
n02843684 birdhouse
n02859443 boathouse
n02860847 bobsled, bobsleigh, bob
n02865351 bolo tie, bolo, bola tie, bola
n02869837 bonnet, poke bonnet
n02870880 bookcase
n02871525 bookshop, bookstore, bookstall
n02877765 bottlecap
n02879718 bow
n02883205 bow tie, bow-tie, bowtie
n02892201 brass, memorial tablet, plaque
n02892767 brassiere, bra, bandeau
n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty
n02895154 breastplate, aegis, egis
n02906734 broom
n02909870 bucket, pail
n02910353 buckle
n02916936 bulletproof vest
n02917067 bullet train, bullet
n02927161 butcher shop, meat market
n02930766 cab, hack, taxi, taxicab
n02939185 caldron, cauldron
n02948072 candle, taper, wax light
n02950826 cannon
n02951358 canoe
n02951585 can opener, tin opener
n02963159 cardigan
n02965783 car mirror
n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig
n02966687 carpenter's kit, tool kit
n02971356 carton
n02974003 car wheel
n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
n02978881 cassette
n02979186 cassette player
n02980441 castle
n02981792 catamaran
n02988304 CD player
n02992211 cello, violoncello
n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone
n02999410 chain
n03000134 chainlink fence
n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
n03000684 chain saw, chainsaw
n03014705 chest
n03016953 chiffonier, commode
n03017168 chime, bell, gong
n03018349 china cabinet, china closet
n03026506 Christmas stocking
n03028079 church, church building
n03032252 cinema, movie theater, movie theatre, movie house, picture palace
n03041632 cleaver, meat cleaver, chopper
n03042490 cliff dwelling
n03045698 cloak
n03047690 clog, geta, patten, sabot
n03062245 cocktail shaker
n03063599 coffee mug
n03063689 coffeepot
n03065424 coil, spiral, volute, whorl, helix
n03075370 combination lock
n03085013 computer keyboard, keypad
n03089624 confectionery, confectionary, candy store
n03095699 container ship, containership, container vessel
n03100240 convertible
n03109150 corkscrew, bottle screw
n03110669 cornet, horn, trumpet, trump
n03124043 cowboy boot
n03124170 cowboy hat, ten-gallon hat
n03125729 cradle
n03126707 crane
n03127747 crash helmet
n03127925 crate
n03131574 crib, cot
n03133878 Crock Pot
n03134739 croquet ball
n03141823 crutch
n03146219 cuirass
n03160309 dam, dike, dyke
n03179701 desk
n03180011 desktop computer
n03187595 dial telephone, dial phone
n03188531 diaper, nappy, napkin
n03196217 digital clock
n03197337 digital watch
n03201208 dining table, board
n03207743 dishrag, dishcloth
n03207941 dishwasher, dish washer, dishwashing machine
n03208938 disk brake, disc brake
n03216828 dock, dockage, docking facility
n03218198 dogsled, dog sled, dog sleigh
n03220513 dome
n03223299 doormat, welcome mat
n03240683 drilling platform, offshore rig
n03249569 drum, membranophone, tympan
n03250847 drumstick
n03255030 dumbbell
n03259280 Dutch oven
n03271574 electric fan, blower
n03272010 electric guitar
n03272562 electric locomotive
n03290653 entertainment center
n03291819 envelope
n03297495 espresso maker
n03314780 face powder
n03325584 feather boa, boa
n03337140 file, file cabinet, filing cabinet
n03344393 fireboat
n03345487 fire engine, fire truck
n03347037 fire screen, fireguard
n03355925 flagpole, flagstaff
n03372029 flute, transverse flute
n03376595 folding chair
n03379051 football helmet
n03384352 forklift
n03388043 fountain
n03388183 fountain pen
n03388549 four-poster
n03393912 freight car
n03394916 French horn, horn
n03400231 frying pan, frypan, skillet
n03404251 fur coat
n03417042 garbage truck, dustcart
n03424325 gasmask, respirator, gas helmet
n03425413 gas pump, gasoline pump, petrol pump, island dispenser
n03443371 goblet
n03444034 go-kart
n03445777 golf ball
n03445924 golfcart, golf cart
n03447447 gondola
n03447721 gong, tam-tam
n03450230 gown
n03452741 grand piano, grand
n03457902 greenhouse, nursery, glasshouse
n03459775 grille, radiator grille
n03461385 grocery store, grocery, food market, market
n03467068 guillotine
n03476684 hair slide
n03476991 hair spray
n03478589 half track
n03481172 hammer
n03482405 hamper
n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier
n03485407 hand-held computer, hand-held microcomputer
n03485794 handkerchief, hankie, hanky, hankey
n03492542 hard disc, hard disk, fixed disk
n03494278 harmonica, mouth organ, harp, mouth harp
n03495258 harp
n03496892 harvester, reaper
n03498962 hatchet
n03527444 holster
n03529860 home theater, home theatre
n03530642 honeycomb
n03532672 hook, claw
n03534580 hoopskirt, crinoline
n03535780 horizontal bar, high bar
n03538406 horse cart, horse-cart
n03544143 hourglass
n03584254 iPod
n03584829 iron, smoothing iron
n03590841 jack-o'-lantern
n03594734 jean, blue jean, denim
n03594945 jeep, landrover
n03595614 jersey, T-shirt, tee shirt
n03598930 jigsaw puzzle
n03599486 jinrikisha, ricksha, rickshaw
n03602883 joystick
n03617480 kimono
n03623198 knee pad
n03627232 knot
n03630383 lab coat, laboratory coat
n03633091 ladle
n03637318 lampshade, lamp shade
n03642806 laptop, laptop computer
n03649909 lawn mower, mower
n03657121 lens cap, lens cover
n03658185 letter opener, paper knife, paperknife
n03661043 library
n03662601 lifeboat
n03666591 lighter, light, igniter, ignitor
n03670208 limousine, limo
n03673027 liner, ocean liner
n03676483 lipstick, lip rouge
n03680355 Loafer
n03690938 lotion
n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
n03692522 loupe, jeweler's loupe
n03697007 lumbermill, sawmill
n03706229 magnetic compass
n03709823 mailbag, postbag
n03710193 mailbox, letter box
n03710637 maillot
n03710721 maillot, tank suit
n03717622 manhole cover
n03720891 maraca
n03721384 marimba, xylophone
n03724870 mask
n03729826 matchstick
n03733131 maypole
n03733281 maze, labyrinth
n03733805 measuring cup
n03742115 medicine chest, medicine cabinet
n03743016 megalith, megalithic structure
n03759954 microphone, mike
n03761084 microwave, microwave oven
n03763968 military uniform
n03764736 milk can
n03769881 minibus
n03770439 miniskirt, mini
n03770679 minivan
n03773504 missile
n03775071 mitten
n03775546 mixing bowl
n03776460 mobile home, manufactured home
n03777568 Model T
n03777754 modem
n03781244 monastery
n03782006 monitor
n03785016 moped
n03786901 mortar
n03787032 mortarboard
n03788195 mosque
n03788365 mosquito net
n03791053 motor scooter, scooter
n03792782 mountain bike, all-terrain bike, off-roader
n03792972 mountain tent
n03793489 mouse, computer mouse
n03794056 mousetrap
n03796401 moving van
n03803284 muzzle
n03804744 nail
n03814639 neck brace
n03814906 necklace
n03825788 nipple
n03832673 notebook, notebook computer
n03837869 obelisk
n03838899 oboe, hautboy, hautbois
n03840681 ocarina, sweet potato
n03841143 odometer, hodometer, mileometer, milometer
n03843555 oil filter
n03854065 organ, pipe organ
n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO
n03866082 overskirt
n03868242 oxcart
n03868863 oxygen mask
n03871628 packet
n03873416 paddle, boat paddle
n03874293 paddlewheel, paddle wheel
n03874599 padlock
n03876231 paintbrush
n03877472 pajama, pyjama, pj's, jammies
n03877845 palace
n03884397 panpipe, pandean pipe, syrinx
n03887697 paper towel
n03888257 parachute, chute
n03888605 parallel bars, bars
n03891251 park bench
n03891332 parking meter
n03895866 passenger car, coach, carriage
n03899768 patio, terrace
n03902125 pay-phone, pay-station
n03903868 pedestal, plinth, footstall
n03908618 pencil box, pencil case
n03908714 pencil sharpener
n03916031 perfume, essence
n03920288 Petri dish
n03924679 photocopier
n03929660 pick, plectrum, plectron
n03929855 pickelhaube
n03930313 picket fence, paling
n03930630 pickup, pickup truck
n03933933 pier
n03935335 piggy bank, penny bank
n03937543 pill bottle
n03938244 pillow
n03942813 ping-pong ball
n03944341 pinwheel
n03947888 pirate, pirate ship
n03950228 pitcher, ewer
n03954731 plane, carpenter's plane, woodworking plane
n03956157 planetarium
n03958227 plastic bag
n03961711 plate rack
n03967562 plow, plough
n03970156 plunger, plumber's helper
n03976467 Polaroid camera, Polaroid Land camera
n03976657 pole
n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
n03980874 poncho
n03982430 pool table, billiard table, snooker table
n03983396 pop bottle, soda bottle
n03991062 pot, flowerpot
n03992509 potter's wheel
n03995372 power drill
n03998194 prayer rug, prayer mat
n04004767 printer
n04005630 prison, prison house
n04008634 projectile, missile
n04009552 projector
n04019541 puck, hockey puck
n04023962 punching bag, punch bag, punching ball, punchball
n04026417 purse
n04033901 quill, quill pen
n04033995 quilt, comforter, comfort, puff
n04037443 racer, race car, racing car
n04039381 racket, racquet
n04040759 radiator
n04041544 radio, wireless
n04044716 radio telescope, radio reflector
n04049303 rain barrel
n04065272 recreational vehicle, RV, R.V.
n04067472 reel
n04069434 reflex camera
n04070727 refrigerator, icebox
n04074963 remote control, remote
n04081281 restaurant, eating house, eating place, eatery
n04086273 revolver, six-gun, six-shooter
n04090263 rifle
n04099969 rocking chair, rocker
n04111531 rotisserie
n04116512 rubber eraser, rubber, pencil eraser
n04118538 rugby ball
n04118776 rule, ruler
n04120489 running shoe
n04125021 safe
n04127249 safety pin
n04131690 saltshaker, salt shaker
n04133789 sandal
n04136333 sarong
n04141076 sax, saxophone
n04141327 scabbard
n04141975 scale, weighing machine
n04146614 school bus
n04147183 schooner
n04149813 scoreboard
n04152593 screen, CRT screen
n04153751 screw
n04154565 screwdriver
n04162706 seat belt, seatbelt
n04179913 sewing machine
n04192698 shield, buckler
n04200800 shoe shop, shoe-shop, shoe store
n04201297 shoji
n04204238 shopping basket
n04204347 shopping cart
n04208210 shovel
n04209133 shower cap
n04209239 shower curtain
n04228054 ski
n04229816 ski mask
n04235860 sleeping bag
n04238763 slide rule, slipstick
n04239074 sliding door
n04243546 slot, one-armed bandit
n04251144 snorkel
n04252077 snowmobile
n04252225 snowplow, snowplough
n04254120 soap dispenser
n04254680 soccer ball
n04254777 sock
n04258138 solar dish, solar collector, solar furnace
n04259630 sombrero
n04263257 soup bowl
n04264628 space bar
n04265275 space heater
n04266014 space shuttle
n04270147 spatula
n04273569 speedboat
n04275548 spider web, spider's web
n04277352 spindle
n04285008 sports car, sport car
n04286575 spotlight, spot
n04296562 stage
n04310018 steam locomotive
n04311004 steel arch bridge
n04311174 steel drum
n04317175 stethoscope
n04325704 stole
n04326547 stone wall
n04328186 stopwatch, stop watch
n04330267 stove
n04332243 strainer
n04335435 streetcar, tram, tramcar, trolley, trolley car
n04336792 stretcher
n04344873 studio couch, day bed
n04346328 stupa, tope
n04347754 submarine, pigboat, sub, U-boat
n04350905 suit, suit of clothes
n04355338 sundial
n04355933 sunglass
n04356056 sunglasses, dark glasses, shades
n04357314 sunscreen, sunblock, sun blocker
n04366367 suspension bridge
n04367480 swab, swob, mop
n04370456 sweatshirt
n04371430 swimming trunks, bathing trunks
n04371774 swing
n04372370 switch, electric switch, electrical switch
n04376876 syringe
n04380533 table lamp
n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle
n04392985 tape player
n04398044 teapot
n04399382 teddy, teddy bear
n04404412 television, television system
n04409515 tennis ball
n04417672 thatch, thatched roof
n04418357 theater curtain, theatre curtain
n04423845 thimble
n04428191 thresher, thrasher, threshing machine
n04429376 throne
n04435653 tile roof
n04442312 toaster
n04443257 tobacco shop, tobacconist shop, tobacconist
n04447861 toilet seat
n04456115 torch
n04458633 totem pole
n04461696 tow truck, tow car, wrecker
n04462240 toyshop
n04465501 tractor
n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
n04476259 tray
n04479046 trench coat
n04482393 tricycle, trike, velocipede
n04483307 trimaran
n04485082 tripod
n04486054 triumphal arch
n04487081 trolleybus, trolley coach, trackless trolley
n04487394 trombone
n04493381 tub, vat
n04501370 turnstile
n04505470 typewriter keyboard
n04507155 umbrella
n04509417 unicycle, monocycle
n04515003 upright, upright piano
n04517823 vacuum, vacuum cleaner
n04522168 vase
n04523525 vault
n04525038 velvet
n04525305 vending machine
n04532106 vestment
n04532670 viaduct
n04536866 violin, fiddle
n04540053 volleyball
n04542943 waffle iron
n04548280 wall clock
n04548362 wallet, billfold, notecase, pocketbook
n04550184 wardrobe, closet, press
n04552348 warplane, military plane
n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin
n04554684 washer, automatic washer, washing machine
n04557648 water bottle
n04560804 water jug
n04562935 water tower
n04579145 whiskey jug
n04579432 whistle
n04584207 wig
n04589890 window screen
n04590129 window shade
n04591157 Windsor tie
n04591713 wine bottle
n04592741 wing
n04596742 wok
n04597913 wooden spoon
n04599235 wool, woolen, woollen
n04604644 worm fence, snake fence, snake-rail fence, Virginia fence
n04606251 wreck
n04612504 yawl
n04613696 yurt
n06359193 web site, website, internet site, site
n06596364 comic book
n06785654 crossword puzzle, crossword
n06794110 street sign
n06874185 traffic light, traffic signal, stoplight
n07248320 book jacket, dust cover, dust jacket, dust wrapper
n07565083 menu
n07579787 plate
n07583066 guacamole
n07584110 consomme
n07590611 hot pot, hotpot
n07613480 trifle
n07614500 ice cream, icecream
n07615774 ice lolly, lolly, lollipop, popsicle
n07684084 French loaf
n07693725 bagel, beigel
n07695742 pretzel
n07697313 cheeseburger
n07697537 hotdog, hot dog, red hot
n07711569 mashed potato
n07714571 head cabbage
n07714990 broccoli
n07715103 cauliflower
n07716358 zucchini, courgette
n07716906 spaghetti squash
n07717410 acorn squash
n07717556 butternut squash
n07718472 cucumber, cuke
n07718747 artichoke, globe artichoke
n07720875 bell pepper
n07730033 cardoon
n07734744 mushroom
n07742313 Granny Smith
n07745940 strawberry
n07747607 orange
n07749582 lemon
n07753113 fig
n07753275 pineapple, ananas
n07753592 banana
n07754684 jackfruit, jak, jack
n07760859 custard apple
n07768694 pomegranate
n07802026 hay
n07831146 carbonara
n07836838 chocolate sauce, chocolate syrup
n07860988 dough
n07871810 meat loaf, meatloaf
n07873807 pizza, pizza pie
n07875152 potpie
n07880968 burrito
n07892512 red wine
n07920052 espresso
n07930864 cup
n07932039 eggnog
n09193705 alp
n09229709 bubble
n09246464 cliff, drop, drop-off
n09256479 coral reef
n09288635 geyser
n09332890 lakeside, lakeshore
n09399592 promontory, headland, head, foreland
n09421951 sandbar, sand bar
n09428293 seashore, coast, seacoast, sea-coast
n09468604 valley, vale
n09472597 volcano
n09835506 ballplayer, baseball player
n10148035 groom, bridegroom
n10565667 scuba diver
n11879895 rapeseed
n11939491 daisy
n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
n12144580 corn
n12267677 acorn
n12620546 hip, rose hip, rosehip
n12768682 buckeye, horse chestnut, conker
n12985857 coral fungus
n12998815 agaric
n13037406 gyromitra
n13040303 stinkhorn, carrion fungus
n13044778 earthstar
n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
n13054560 bolete
n13133613 ear, spike, capitulum
n15075141 toilet tissue, toilet paper, bathroom tissue
``` | mrm8488/ImageNet1K-val | [
"region:us"
] | 2022-04-27T18:05:28+00:00 | {} | 2022-04-27T18:16:51+00:00 |
006c4bc87abe217e728bbd7bfcd76f9f828c11e2 | mapping:
```
n01440764 tench, Tinca tinca
n01443537 goldfish, Carassius auratus
n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
n01491361 tiger shark, Galeocerdo cuvieri
n01494475 hammerhead, hammerhead shark
n01496331 electric ray, crampfish, numbfish, torpedo
n01498041 stingray
n01514668 cock
n01514859 hen
n01518878 ostrich, Struthio camelus
n01530575 brambling, Fringilla montifringilla
n01531178 goldfinch, Carduelis carduelis
n01532829 house finch, linnet, Carpodacus mexicanus
n01534433 junco, snowbird
n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea
n01558993 robin, American robin, Turdus migratorius
n01560419 bulbul
n01580077 jay
n01582220 magpie
n01592084 chickadee
n01601694 water ouzel, dipper
n01608432 kite
n01614925 bald eagle, American eagle, Haliaeetus leucocephalus
n01616318 vulture
n01622779 great grey owl, great gray owl, Strix nebulosa
n01629819 European fire salamander, Salamandra salamandra
n01630670 common newt, Triturus vulgaris
n01631663 eft
n01632458 spotted salamander, Ambystoma maculatum
n01632777 axolotl, mud puppy, Ambystoma mexicanum
n01641577 bullfrog, Rana catesbeiana
n01644373 tree frog, tree-frog
n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
n01664065 loggerhead, loggerhead turtle, Caretta caretta
n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
n01667114 mud turtle
n01667778 terrapin
n01669191 box turtle, box tortoise
n01675722 banded gecko
n01677366 common iguana, iguana, Iguana iguana
n01682714 American chameleon, anole, Anolis carolinensis
n01685808 whiptail, whiptail lizard
n01687978 agama
n01688243 frilled lizard, Chlamydosaurus kingi
n01689811 alligator lizard
n01692333 Gila monster, Heloderma suspectum
n01693334 green lizard, Lacerta viridis
n01694178 African chameleon, Chamaeleo chamaeleon
n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
n01697457 African crocodile, Nile crocodile, Crocodylus niloticus
n01698640 American alligator, Alligator mississipiensis
n01704323 triceratops
n01728572 thunder snake, worm snake, Carphophis amoenus
n01728920 ringneck snake, ring-necked snake, ring snake
n01729322 hognose snake, puff adder, sand viper
n01729977 green snake, grass snake
n01734418 king snake, kingsnake
n01735189 garter snake, grass snake
n01737021 water snake
n01739381 vine snake
n01740131 night snake, Hypsiglena torquata
n01742172 boa constrictor, Constrictor constrictor
n01744401 rock python, rock snake, Python sebae
n01748264 Indian cobra, Naja naja
n01749939 green mamba
n01751748 sea snake
n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus
n01756291 sidewinder, horned rattlesnake, Crotalus cerastes
n01768244 trilobite
n01770081 harvestman, daddy longlegs, Phalangium opilio
n01770393 scorpion
n01773157 black and gold garden spider, Argiope aurantia
n01773549 barn spider, Araneus cavaticus
n01773797 garden spider, Aranea diademata
n01774384 black widow, Latrodectus mactans
n01774750 tarantula
n01775062 wolf spider, hunting spider
n01776313 tick
n01784675 centipede
n01795545 black grouse
n01796340 ptarmigan
n01797886 ruffed grouse, partridge, Bonasa umbellus
n01798484 prairie chicken, prairie grouse, prairie fowl
n01806143 peacock
n01806567 quail
n01807496 partridge
n01817953 African grey, African gray, Psittacus erithacus
n01818515 macaw
n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
n01820546 lorikeet
n01824575 coucal
n01828970 bee eater
n01829413 hornbill
n01833805 hummingbird
n01843065 jacamar
n01843383 toucan
n01847000 drake
n01855032 red-breasted merganser, Mergus serrator
n01855672 goose
n01860187 black swan, Cygnus atratus
n01871265 tusker
n01872401 echidna, spiny anteater, anteater
n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
n01877812 wallaby, brush kangaroo
n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
n01883070 wombat
n01910747 jellyfish
n01914609 sea anemone, anemone
n01917289 brain coral
n01924916 flatworm, platyhelminth
n01930112 nematode, nematode worm, roundworm
n01943899 conch
n01944390 snail
n01945685 slug
n01950731 sea slug, nudibranch
n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore
n01968897 chambered nautilus, pearly nautilus, nautilus
n01978287 Dungeness crab, Cancer magister
n01978455 rock crab, Cancer irroratus
n01980166 fiddler crab
n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus
n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
n01985128 crayfish, crawfish, crawdad, crawdaddy
n01986214 hermit crab
n01990800 isopod
n02002556 white stork, Ciconia ciconia
n02002724 black stork, Ciconia nigra
n02006656 spoonbill
n02007558 flamingo
n02009229 little blue heron, Egretta caerulea
n02009912 American egret, great white heron, Egretta albus
n02011460 bittern
n02012849 crane
n02013706 limpkin, Aramus pictus
n02017213 European gallinule, Porphyrio porphyrio
n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana
n02018795 bustard
n02025239 ruddy turnstone, Arenaria interpres
n02027492 red-backed sandpiper, dunlin, Erolia alpina
n02028035 redshank, Tringa totanus
n02033041 dowitcher
n02037110 oystercatcher, oyster catcher
n02051845 pelican
n02056570 king penguin, Aptenodytes patagonica
n02058221 albatross, mollymawk
n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca
n02074367 dugong, Dugong dugon
n02077923 sea lion
n02085620 Chihuahua
n02085782 Japanese spaniel
n02085936 Maltese dog, Maltese terrier, Maltese
n02086079 Pekinese, Pekingese, Peke
n02086240 Shih-Tzu
n02086646 Blenheim spaniel
n02086910 papillon
n02087046 toy terrier
n02087394 Rhodesian ridgeback
n02088094 Afghan hound, Afghan
n02088238 basset, basset hound
n02088364 beagle
n02088466 bloodhound, sleuthhound
n02088632 bluetick
n02089078 black-and-tan coonhound
n02089867 Walker hound, Walker foxhound
n02089973 English foxhound
n02090379 redbone
n02090622 borzoi, Russian wolfhound
n02090721 Irish wolfhound
n02091032 Italian greyhound
n02091134 whippet
n02091244 Ibizan hound, Ibizan Podenco
n02091467 Norwegian elkhound, elkhound
n02091635 otterhound, otter hound
n02091831 Saluki, gazelle hound
n02092002 Scottish deerhound, deerhound
n02092339 Weimaraner
n02093256 Staffordshire bullterrier, Staffordshire bull terrier
n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
n02093647 Bedlington terrier
n02093754 Border terrier
n02093859 Kerry blue terrier
n02093991 Irish terrier
n02094114 Norfolk terrier
n02094258 Norwich terrier
n02094433 Yorkshire terrier
n02095314 wire-haired fox terrier
n02095570 Lakeland terrier
n02095889 Sealyham terrier, Sealyham
n02096051 Airedale, Airedale terrier
n02096177 cairn, cairn terrier
n02096294 Australian terrier
n02096437 Dandie Dinmont, Dandie Dinmont terrier
n02096585 Boston bull, Boston terrier
n02097047 miniature schnauzer
n02097130 giant schnauzer
n02097209 standard schnauzer
n02097298 Scotch terrier, Scottish terrier, Scottie
n02097474 Tibetan terrier, chrysanthemum dog
n02097658 silky terrier, Sydney silky
n02098105 soft-coated wheaten terrier
n02098286 West Highland white terrier
n02098413 Lhasa, Lhasa apso
n02099267 flat-coated retriever
n02099429 curly-coated retriever
n02099601 golden retriever
n02099712 Labrador retriever
n02099849 Chesapeake Bay retriever
n02100236 German short-haired pointer
n02100583 vizsla, Hungarian pointer
n02100735 English setter
n02100877 Irish setter, red setter
n02101006 Gordon setter
n02101388 Brittany spaniel
n02101556 clumber, clumber spaniel
n02102040 English springer, English springer spaniel
n02102177 Welsh springer spaniel
n02102318 cocker spaniel, English cocker spaniel, cocker
n02102480 Sussex spaniel
n02102973 Irish water spaniel
n02104029 kuvasz
n02104365 schipperke
n02105056 groenendael
n02105162 malinois
n02105251 briard
n02105412 kelpie
n02105505 komondor
n02105641 Old English sheepdog, bobtail
n02105855 Shetland sheepdog, Shetland sheep dog, Shetland
n02106030 collie
n02106166 Border collie
n02106382 Bouvier des Flandres, Bouviers des Flandres
n02106550 Rottweiler
n02106662 German shepherd, German shepherd dog, German police dog, alsatian
n02107142 Doberman, Doberman pinscher
n02107312 miniature pinscher
n02107574 Greater Swiss Mountain dog
n02107683 Bernese mountain dog
n02107908 Appenzeller
n02108000 EntleBucher
n02108089 boxer
n02108422 bull mastiff
n02108551 Tibetan mastiff
n02108915 French bulldog
n02109047 Great Dane
n02109525 Saint Bernard, St Bernard
n02109961 Eskimo dog, husky
n02110063 malamute, malemute, Alaskan malamute
n02110185 Siberian husky
n02110341 dalmatian, coach dog, carriage dog
n02110627 affenpinscher, monkey pinscher, monkey dog
n02110806 basenji
n02110958 pug, pug-dog
n02111129 Leonberg
n02111277 Newfoundland, Newfoundland dog
n02111500 Great Pyrenees
n02111889 Samoyed, Samoyede
n02112018 Pomeranian
n02112137 chow, chow chow
n02112350 keeshond
n02112706 Brabancon griffon
n02113023 Pembroke, Pembroke Welsh corgi
n02113186 Cardigan, Cardigan Welsh corgi
n02113624 toy poodle
n02113712 miniature poodle
n02113799 standard poodle
n02113978 Mexican hairless
n02114367 timber wolf, grey wolf, gray wolf, Canis lupus
n02114548 white wolf, Arctic wolf, Canis lupus tundrarum
n02114712 red wolf, maned wolf, Canis rufus, Canis niger
n02114855 coyote, prairie wolf, brush wolf, Canis latrans
n02115641 dingo, warrigal, warragal, Canis dingo
n02115913 dhole, Cuon alpinus
n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
n02117135 hyena, hyaena
n02119022 red fox, Vulpes vulpes
n02119789 kit fox, Vulpes macrotis
n02120079 Arctic fox, white fox, Alopex lagopus
n02120505 grey fox, gray fox, Urocyon cinereoargenteus
n02123045 tabby, tabby cat
n02123159 tiger cat
n02123394 Persian cat
n02123597 Siamese cat, Siamese
n02124075 Egyptian cat
n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
n02127052 lynx, catamount
n02128385 leopard, Panthera pardus
n02128757 snow leopard, ounce, Panthera uncia
n02128925 jaguar, panther, Panthera onca, Felis onca
n02129165 lion, king of beasts, Panthera leo
n02129604 tiger, Panthera tigris
n02130308 cheetah, chetah, Acinonyx jubatus
n02132136 brown bear, bruin, Ursus arctos
n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus
n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
n02134418 sloth bear, Melursus ursinus, Ursus ursinus
n02137549 mongoose
n02138441 meerkat, mierkat
n02165105 tiger beetle
n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
n02167151 ground beetle, carabid beetle
n02168699 long-horned beetle, longicorn, longicorn beetle
n02169497 leaf beetle, chrysomelid
n02172182 dung beetle
n02174001 rhinoceros beetle
n02177972 weevil
n02190166 fly
n02206856 bee
n02219486 ant, emmet, pismire
n02226429 grasshopper, hopper
n02229544 cricket
n02231487 walking stick, walkingstick, stick insect
n02233338 cockroach, roach
n02236044 mantis, mantid
n02256656 cicada, cicala
n02259212 leafhopper
n02264363 lacewing, lacewing fly
n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
n02268853 damselfly
n02276258 admiral
n02277742 ringlet, ringlet butterfly
n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
n02280649 cabbage butterfly
n02281406 sulphur butterfly, sulfur butterfly
n02281787 lycaenid, lycaenid butterfly
n02317335 starfish, sea star
n02319095 sea urchin
n02321529 sea cucumber, holothurian
n02325366 wood rabbit, cottontail, cottontail rabbit
n02326432 hare
n02328150 Angora, Angora rabbit
n02342885 hamster
n02346627 porcupine, hedgehog
n02356798 fox squirrel, eastern fox squirrel, Sciurus niger
n02361337 marmot
n02363005 beaver
n02364673 guinea pig, Cavia cobaya
n02389026 sorrel
n02391049 zebra
n02395406 hog, pig, grunter, squealer, Sus scrofa
n02396427 wild boar, boar, Sus scrofa
n02397096 warthog
n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius
n02403003 ox
n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
n02410509 bison
n02412080 ram, tup
n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
n02417914 ibex, Capra ibex
n02422106 hartebeest
n02422699 impala, Aepyceros melampus
n02423022 gazelle
n02437312 Arabian camel, dromedary, Camelus dromedarius
n02437616 llama
n02441942 weasel
n02442845 mink
n02443114 polecat, fitch, foulmart, foumart, Mustela putorius
n02443484 black-footed ferret, ferret, Mustela nigripes
n02444819 otter
n02445715 skunk, polecat, wood pussy
n02447366 badger
n02454379 armadillo
n02457408 three-toed sloth, ai, Bradypus tridactylus
n02480495 orangutan, orang, orangutang, Pongo pygmaeus
n02480855 gorilla, Gorilla gorilla
n02481823 chimpanzee, chimp, Pan troglodytes
n02483362 gibbon, Hylobates lar
n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus
n02484975 guenon, guenon monkey
n02486261 patas, hussar monkey, Erythrocebus patas
n02486410 baboon
n02487347 macaque
n02488291 langur
n02488702 colobus, colobus monkey
n02489166 proboscis monkey, Nasalis larvatus
n02490219 marmoset
n02492035 capuchin, ringtail, Cebus capucinus
n02492660 howler monkey, howler
n02493509 titi, titi monkey
n02493793 spider monkey, Ateles geoffroyi
n02494079 squirrel monkey, Saimiri sciureus
n02497673 Madagascar cat, ring-tailed lemur, Lemur catta
n02500267 indri, indris, Indri indri, Indri brevicaudatus
n02504013 Indian elephant, Elephas maximus
n02504458 African elephant, Loxodonta africana
n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
n02514041 barracouta, snoek
n02526121 eel
n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
n02606052 rock beauty, Holocanthus tricolor
n02607072 anemone fish
n02640242 sturgeon
n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus
n02643566 lionfish
n02655020 puffer, pufferfish, blowfish, globefish
n02666196 abacus
n02667093 abaya
n02669723 academic gown, academic robe, judge's robe
n02672831 accordion, piano accordion, squeeze box
n02676566 acoustic guitar
n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier
n02690373 airliner
n02692877 airship, dirigible
n02699494 altar
n02701002 ambulance
n02704792 amphibian, amphibious vehicle
n02708093 analog clock
n02727426 apiary, bee house
n02730930 apron
n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
n02749479 assault rifle, assault gun
n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack
n02776631 bakery, bakeshop, bakehouse
n02777292 balance beam, beam
n02782093 balloon
n02783161 ballpoint, ballpoint pen, ballpen, Biro
n02786058 Band Aid
n02787622 banjo
n02788148 bannister, banister, balustrade, balusters, handrail
n02790996 barbell
n02791124 barber chair
n02791270 barbershop
n02793495 barn
n02794156 barometer
n02795169 barrel, cask
n02797295 barrow, garden cart, lawn cart, wheelbarrow
n02799071 baseball
n02802426 basketball
n02804414 bassinet
n02804610 bassoon
n02807133 bathing cap, swimming cap
n02808304 bath towel
n02808440 bathtub, bathing tub, bath, tub
n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
n02814860 beacon, lighthouse, beacon light, pharos
n02815834 beaker
n02817516 bearskin, busby, shako
n02823428 beer bottle
n02823750 beer glass
n02825657 bell cote, bell cot
n02834397 bib
n02835271 bicycle-built-for-two, tandem bicycle, tandem
n02837789 bikini, two-piece
n02840245 binder, ring-binder
n02841315 binoculars, field glasses, opera glasses
n02843684 birdhouse
n02859443 boathouse
n02860847 bobsled, bobsleigh, bob
n02865351 bolo tie, bolo, bola tie, bola
n02869837 bonnet, poke bonnet
n02870880 bookcase
n02871525 bookshop, bookstore, bookstall
n02877765 bottlecap
n02879718 bow
n02883205 bow tie, bow-tie, bowtie
n02892201 brass, memorial tablet, plaque
n02892767 brassiere, bra, bandeau
n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty
n02895154 breastplate, aegis, egis
n02906734 broom
n02909870 bucket, pail
n02910353 buckle
n02916936 bulletproof vest
n02917067 bullet train, bullet
n02927161 butcher shop, meat market
n02930766 cab, hack, taxi, taxicab
n02939185 caldron, cauldron
n02948072 candle, taper, wax light
n02950826 cannon
n02951358 canoe
n02951585 can opener, tin opener
n02963159 cardigan
n02965783 car mirror
n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig
n02966687 carpenter's kit, tool kit
n02971356 carton
n02974003 car wheel
n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
n02978881 cassette
n02979186 cassette player
n02980441 castle
n02981792 catamaran
n02988304 CD player
n02992211 cello, violoncello
n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone
n02999410 chain
n03000134 chainlink fence
n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
n03000684 chain saw, chainsaw
n03014705 chest
n03016953 chiffonier, commode
n03017168 chime, bell, gong
n03018349 china cabinet, china closet
n03026506 Christmas stocking
n03028079 church, church building
n03032252 cinema, movie theater, movie theatre, movie house, picture palace
n03041632 cleaver, meat cleaver, chopper
n03042490 cliff dwelling
n03045698 cloak
n03047690 clog, geta, patten, sabot
n03062245 cocktail shaker
n03063599 coffee mug
n03063689 coffeepot
n03065424 coil, spiral, volute, whorl, helix
n03075370 combination lock
n03085013 computer keyboard, keypad
n03089624 confectionery, confectionary, candy store
n03095699 container ship, containership, container vessel
n03100240 convertible
n03109150 corkscrew, bottle screw
n03110669 cornet, horn, trumpet, trump
n03124043 cowboy boot
n03124170 cowboy hat, ten-gallon hat
n03125729 cradle
n03126707 crane
n03127747 crash helmet
n03127925 crate
n03131574 crib, cot
n03133878 Crock Pot
n03134739 croquet ball
n03141823 crutch
n03146219 cuirass
n03160309 dam, dike, dyke
n03179701 desk
n03180011 desktop computer
n03187595 dial telephone, dial phone
n03188531 diaper, nappy, napkin
n03196217 digital clock
n03197337 digital watch
n03201208 dining table, board
n03207743 dishrag, dishcloth
n03207941 dishwasher, dish washer, dishwashing machine
n03208938 disk brake, disc brake
n03216828 dock, dockage, docking facility
n03218198 dogsled, dog sled, dog sleigh
n03220513 dome
n03223299 doormat, welcome mat
n03240683 drilling platform, offshore rig
n03249569 drum, membranophone, tympan
n03250847 drumstick
n03255030 dumbbell
n03259280 Dutch oven
n03271574 electric fan, blower
n03272010 electric guitar
n03272562 electric locomotive
n03290653 entertainment center
n03291819 envelope
n03297495 espresso maker
n03314780 face powder
n03325584 feather boa, boa
n03337140 file, file cabinet, filing cabinet
n03344393 fireboat
n03345487 fire engine, fire truck
n03347037 fire screen, fireguard
n03355925 flagpole, flagstaff
n03372029 flute, transverse flute
n03376595 folding chair
n03379051 football helmet
n03384352 forklift
n03388043 fountain
n03388183 fountain pen
n03388549 four-poster
n03393912 freight car
n03394916 French horn, horn
n03400231 frying pan, frypan, skillet
n03404251 fur coat
n03417042 garbage truck, dustcart
n03424325 gasmask, respirator, gas helmet
n03425413 gas pump, gasoline pump, petrol pump, island dispenser
n03443371 goblet
n03444034 go-kart
n03445777 golf ball
n03445924 golfcart, golf cart
n03447447 gondola
n03447721 gong, tam-tam
n03450230 gown
n03452741 grand piano, grand
n03457902 greenhouse, nursery, glasshouse
n03459775 grille, radiator grille
n03461385 grocery store, grocery, food market, market
n03467068 guillotine
n03476684 hair slide
n03476991 hair spray
n03478589 half track
n03481172 hammer
n03482405 hamper
n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier
n03485407 hand-held computer, hand-held microcomputer
n03485794 handkerchief, hankie, hanky, hankey
n03492542 hard disc, hard disk, fixed disk
n03494278 harmonica, mouth organ, harp, mouth harp
n03495258 harp
n03496892 harvester, reaper
n03498962 hatchet
n03527444 holster
n03529860 home theater, home theatre
n03530642 honeycomb
n03532672 hook, claw
n03534580 hoopskirt, crinoline
n03535780 horizontal bar, high bar
n03538406 horse cart, horse-cart
n03544143 hourglass
n03584254 iPod
n03584829 iron, smoothing iron
n03590841 jack-o'-lantern
n03594734 jean, blue jean, denim
n03594945 jeep, landrover
n03595614 jersey, T-shirt, tee shirt
n03598930 jigsaw puzzle
n03599486 jinrikisha, ricksha, rickshaw
n03602883 joystick
n03617480 kimono
n03623198 knee pad
n03627232 knot
n03630383 lab coat, laboratory coat
n03633091 ladle
n03637318 lampshade, lamp shade
n03642806 laptop, laptop computer
n03649909 lawn mower, mower
n03657121 lens cap, lens cover
n03658185 letter opener, paper knife, paperknife
n03661043 library
n03662601 lifeboat
n03666591 lighter, light, igniter, ignitor
n03670208 limousine, limo
n03673027 liner, ocean liner
n03676483 lipstick, lip rouge
n03680355 Loafer
n03690938 lotion
n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
n03692522 loupe, jeweler's loupe
n03697007 lumbermill, sawmill
n03706229 magnetic compass
n03709823 mailbag, postbag
n03710193 mailbox, letter box
n03710637 maillot
n03710721 maillot, tank suit
n03717622 manhole cover
n03720891 maraca
n03721384 marimba, xylophone
n03724870 mask
n03729826 matchstick
n03733131 maypole
n03733281 maze, labyrinth
n03733805 measuring cup
n03742115 medicine chest, medicine cabinet
n03743016 megalith, megalithic structure
n03759954 microphone, mike
n03761084 microwave, microwave oven
n03763968 military uniform
n03764736 milk can
n03769881 minibus
n03770439 miniskirt, mini
n03770679 minivan
n03773504 missile
n03775071 mitten
n03775546 mixing bowl
n03776460 mobile home, manufactured home
n03777568 Model T
n03777754 modem
n03781244 monastery
n03782006 monitor
n03785016 moped
n03786901 mortar
n03787032 mortarboard
n03788195 mosque
n03788365 mosquito net
n03791053 motor scooter, scooter
n03792782 mountain bike, all-terrain bike, off-roader
n03792972 mountain tent
n03793489 mouse, computer mouse
n03794056 mousetrap
n03796401 moving van
n03803284 muzzle
n03804744 nail
n03814639 neck brace
n03814906 necklace
n03825788 nipple
n03832673 notebook, notebook computer
n03837869 obelisk
n03838899 oboe, hautboy, hautbois
n03840681 ocarina, sweet potato
n03841143 odometer, hodometer, mileometer, milometer
n03843555 oil filter
n03854065 organ, pipe organ
n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO
n03866082 overskirt
n03868242 oxcart
n03868863 oxygen mask
n03871628 packet
n03873416 paddle, boat paddle
n03874293 paddlewheel, paddle wheel
n03874599 padlock
n03876231 paintbrush
n03877472 pajama, pyjama, pj's, jammies
n03877845 palace
n03884397 panpipe, pandean pipe, syrinx
n03887697 paper towel
n03888257 parachute, chute
n03888605 parallel bars, bars
n03891251 park bench
n03891332 parking meter
n03895866 passenger car, coach, carriage
n03899768 patio, terrace
n03902125 pay-phone, pay-station
n03903868 pedestal, plinth, footstall
n03908618 pencil box, pencil case
n03908714 pencil sharpener
n03916031 perfume, essence
n03920288 Petri dish
n03924679 photocopier
n03929660 pick, plectrum, plectron
n03929855 pickelhaube
n03930313 picket fence, paling
n03930630 pickup, pickup truck
n03933933 pier
n03935335 piggy bank, penny bank
n03937543 pill bottle
n03938244 pillow
n03942813 ping-pong ball
n03944341 pinwheel
n03947888 pirate, pirate ship
n03950228 pitcher, ewer
n03954731 plane, carpenter's plane, woodworking plane
n03956157 planetarium
n03958227 plastic bag
n03961711 plate rack
n03967562 plow, plough
n03970156 plunger, plumber's helper
n03976467 Polaroid camera, Polaroid Land camera
n03976657 pole
n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
n03980874 poncho
n03982430 pool table, billiard table, snooker table
n03983396 pop bottle, soda bottle
n03991062 pot, flowerpot
n03992509 potter's wheel
n03995372 power drill
n03998194 prayer rug, prayer mat
n04004767 printer
n04005630 prison, prison house
n04008634 projectile, missile
n04009552 projector
n04019541 puck, hockey puck
n04023962 punching bag, punch bag, punching ball, punchball
n04026417 purse
n04033901 quill, quill pen
n04033995 quilt, comforter, comfort, puff
n04037443 racer, race car, racing car
n04039381 racket, racquet
n04040759 radiator
n04041544 radio, wireless
n04044716 radio telescope, radio reflector
n04049303 rain barrel
n04065272 recreational vehicle, RV, R.V.
n04067472 reel
n04069434 reflex camera
n04070727 refrigerator, icebox
n04074963 remote control, remote
n04081281 restaurant, eating house, eating place, eatery
n04086273 revolver, six-gun, six-shooter
n04090263 rifle
n04099969 rocking chair, rocker
n04111531 rotisserie
n04116512 rubber eraser, rubber, pencil eraser
n04118538 rugby ball
n04118776 rule, ruler
n04120489 running shoe
n04125021 safe
n04127249 safety pin
n04131690 saltshaker, salt shaker
n04133789 sandal
n04136333 sarong
n04141076 sax, saxophone
n04141327 scabbard
n04141975 scale, weighing machine
n04146614 school bus
n04147183 schooner
n04149813 scoreboard
n04152593 screen, CRT screen
n04153751 screw
n04154565 screwdriver
n04162706 seat belt, seatbelt
n04179913 sewing machine
n04192698 shield, buckler
n04200800 shoe shop, shoe-shop, shoe store
n04201297 shoji
n04204238 shopping basket
n04204347 shopping cart
n04208210 shovel
n04209133 shower cap
n04209239 shower curtain
n04228054 ski
n04229816 ski mask
n04235860 sleeping bag
n04238763 slide rule, slipstick
n04239074 sliding door
n04243546 slot, one-armed bandit
n04251144 snorkel
n04252077 snowmobile
n04252225 snowplow, snowplough
n04254120 soap dispenser
n04254680 soccer ball
n04254777 sock
n04258138 solar dish, solar collector, solar furnace
n04259630 sombrero
n04263257 soup bowl
n04264628 space bar
n04265275 space heater
n04266014 space shuttle
n04270147 spatula
n04273569 speedboat
n04275548 spider web, spider's web
n04277352 spindle
n04285008 sports car, sport car
n04286575 spotlight, spot
n04296562 stage
n04310018 steam locomotive
n04311004 steel arch bridge
n04311174 steel drum
n04317175 stethoscope
n04325704 stole
n04326547 stone wall
n04328186 stopwatch, stop watch
n04330267 stove
n04332243 strainer
n04335435 streetcar, tram, tramcar, trolley, trolley car
n04336792 stretcher
n04344873 studio couch, day bed
n04346328 stupa, tope
n04347754 submarine, pigboat, sub, U-boat
n04350905 suit, suit of clothes
n04355338 sundial
n04355933 sunglass
n04356056 sunglasses, dark glasses, shades
n04357314 sunscreen, sunblock, sun blocker
n04366367 suspension bridge
n04367480 swab, swob, mop
n04370456 sweatshirt
n04371430 swimming trunks, bathing trunks
n04371774 swing
n04372370 switch, electric switch, electrical switch
n04376876 syringe
n04380533 table lamp
n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle
n04392985 tape player
n04398044 teapot
n04399382 teddy, teddy bear
n04404412 television, television system
n04409515 tennis ball
n04417672 thatch, thatched roof
n04418357 theater curtain, theatre curtain
n04423845 thimble
n04428191 thresher, thrasher, threshing machine
n04429376 throne
n04435653 tile roof
n04442312 toaster
n04443257 tobacco shop, tobacconist shop, tobacconist
n04447861 toilet seat
n04456115 torch
n04458633 totem pole
n04461696 tow truck, tow car, wrecker
n04462240 toyshop
n04465501 tractor
n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
n04476259 tray
n04479046 trench coat
n04482393 tricycle, trike, velocipede
n04483307 trimaran
n04485082 tripod
n04486054 triumphal arch
n04487081 trolleybus, trolley coach, trackless trolley
n04487394 trombone
n04493381 tub, vat
n04501370 turnstile
n04505470 typewriter keyboard
n04507155 umbrella
n04509417 unicycle, monocycle
n04515003 upright, upright piano
n04517823 vacuum, vacuum cleaner
n04522168 vase
n04523525 vault
n04525038 velvet
n04525305 vending machine
n04532106 vestment
n04532670 viaduct
n04536866 violin, fiddle
n04540053 volleyball
n04542943 waffle iron
n04548280 wall clock
n04548362 wallet, billfold, notecase, pocketbook
n04550184 wardrobe, closet, press
n04552348 warplane, military plane
n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin
n04554684 washer, automatic washer, washing machine
n04557648 water bottle
n04560804 water jug
n04562935 water tower
n04579145 whiskey jug
n04579432 whistle
n04584207 wig
n04589890 window screen
n04590129 window shade
n04591157 Windsor tie
n04591713 wine bottle
n04592741 wing
n04596742 wok
n04597913 wooden spoon
n04599235 wool, woolen, woollen
n04604644 worm fence, snake fence, snake-rail fence, Virginia fence
n04606251 wreck
n04612504 yawl
n04613696 yurt
n06359193 web site, website, internet site, site
n06596364 comic book
n06785654 crossword puzzle, crossword
n06794110 street sign
n06874185 traffic light, traffic signal, stoplight
n07248320 book jacket, dust cover, dust jacket, dust wrapper
n07565083 menu
n07579787 plate
n07583066 guacamole
n07584110 consomme
n07590611 hot pot, hotpot
n07613480 trifle
n07614500 ice cream, icecream
n07615774 ice lolly, lolly, lollipop, popsicle
n07684084 French loaf
n07693725 bagel, beigel
n07695742 pretzel
n07697313 cheeseburger
n07697537 hotdog, hot dog, red hot
n07711569 mashed potato
n07714571 head cabbage
n07714990 broccoli
n07715103 cauliflower
n07716358 zucchini, courgette
n07716906 spaghetti squash
n07717410 acorn squash
n07717556 butternut squash
n07718472 cucumber, cuke
n07718747 artichoke, globe artichoke
n07720875 bell pepper
n07730033 cardoon
n07734744 mushroom
n07742313 Granny Smith
n07745940 strawberry
n07747607 orange
n07749582 lemon
n07753113 fig
n07753275 pineapple, ananas
n07753592 banana
n07754684 jackfruit, jak, jack
n07760859 custard apple
n07768694 pomegranate
n07802026 hay
n07831146 carbonara
n07836838 chocolate sauce, chocolate syrup
n07860988 dough
n07871810 meat loaf, meatloaf
n07873807 pizza, pizza pie
n07875152 potpie
n07880968 burrito
n07892512 red wine
n07920052 espresso
n07930864 cup
n07932039 eggnog
n09193705 alp
n09229709 bubble
n09246464 cliff, drop, drop-off
n09256479 coral reef
n09288635 geyser
n09332890 lakeside, lakeshore
n09399592 promontory, headland, head, foreland
n09421951 sandbar, sand bar
n09428293 seashore, coast, seacoast, sea-coast
n09468604 valley, vale
n09472597 volcano
n09835506 ballplayer, baseball player
n10148035 groom, bridegroom
n10565667 scuba diver
n11879895 rapeseed
n11939491 daisy
n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
n12144580 corn
n12267677 acorn
n12620546 hip, rose hip, rosehip
n12768682 buckeye, horse chestnut, conker
n12985857 coral fungus
n12998815 agaric
n13037406 gyromitra
n13040303 stinkhorn, carrion fungus
n13044778 earthstar
n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
n13054560 bolete
n13133613 ear, spike, capitulum
n15075141 toilet tissue, toilet paper, bathroom tissue
``` | mrm8488/ImageNet1K-train | [
"region:us"
] | 2022-04-27T19:03:48+00:00 | {} | 2022-04-28T10:06:11+00:00 |
ff6bd8e4b27c3543e4f8fe2108f32bb95a6f8740 |
# MASSIVE 1.1: A 1M-Example Multilingual Natural Language Understanding Dataset with 52 Typologically-Diverse Languages
## Table of Contents
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [No Warranty](#no-warranty)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/alexa/massive
- **Repository:** https://github.com/alexa/massive
- **Paper:** https://arxiv.org/abs/2204.08582
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview
- **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues)
### Dataset Summary
MASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
| Name | Lang | Utt/Lang | Domains | Intents | Slots |
|:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:|
| MASSIVE 1.1 | 52 | 19,521 | 18 | 60 | 55 |
| SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 |
| NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 |
| Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 |
| ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 |
| MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 |
| Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 |
| Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 |
| Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 |
| Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 |
| Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 |
| Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 |
| Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - |
| Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 |
### Supported Tasks and Leaderboards
The dataset can be used to train a model for `natural-language-understanding` (NLU) :
- `intent-classification`
- `multi-class-classification`
- `natural-language-understanding`
### Languages
The MASSIVE 1.1 corpora consists of parallel sentences from 52 languages :
- `Afrikaans - South Africa (af-ZA)`
- `Amharic - Ethiopia (am-ET)`
- `Arabic - Saudi Arabia (ar-SA)`
- `Azeri - Azerbaijan (az-AZ)`
- `Bengali - Bangladesh (bn-BD)`
- `Catalan - Spain (ca-ES)`
- `Chinese - China (zh-CN)`
- `Chinese - Taiwan (zh-TW)`
- `Danish - Denmark (da-DK)`
- `German - Germany (de-DE)`
- `Greek - Greece (el-GR)`
- `English - United States (en-US)`
- `Spanish - Spain (es-ES)`
- `Farsi - Iran (fa-IR)`
- `Finnish - Finland (fi-FI)`
- `French - France (fr-FR)`
- `Hebrew - Israel (he-IL)`
- `Hungarian - Hungary (hu-HU)`
- `Armenian - Armenia (hy-AM)`
- `Indonesian - Indonesia (id-ID)`
- `Icelandic - Iceland (is-IS)`
- `Italian - Italy (it-IT)`
- `Japanese - Japan (ja-JP)`
- `Javanese - Indonesia (jv-ID)`
- `Georgian - Georgia (ka-GE)`
- `Khmer - Cambodia (km-KH)`
- `Korean - Korea (ko-KR)`
- `Latvian - Latvia (lv-LV)`
- `Mongolian - Mongolia (mn-MN)`
- `Malay - Malaysia (ms-MY)`
- `Burmese - Myanmar (my-MM)`
- `Norwegian - Norway (nb-NO)`
- `Dutch - Netherlands (nl-NL)`
- `Polish - Poland (pl-PL)`
- `Portuguese - Portugal (pt-PT)`
- `Romanian - Romania (ro-RO)`
- `Russian - Russia (ru-RU)`
- `Slovanian - Slovania (sl-SL)`
- `Albanian - Albania (sq-AL)`
- `Swedish - Sweden (sv-SE)`
- `Swahili - Kenya (sw-KE)`
- `Hindi - India (hi-IN)`
- `Kannada - India (kn-IN)`
- `Malayalam - India (ml-IN)`
- `Tamil - India (ta-IN)`
- `Telugu - India (te-IN)`
- `Thai - Thailand (th-TH)`
- `Tagalog - Philippines (tl-PH)`
- `Turkish - Turkey (tr-TR)`
- `Urdu - Pakistan (ur-PK)`
- `Vietnamese - Vietnam (vi-VN)`
- `Welsh - United Kingdom (cy-GB)`
## Load the dataset with HuggingFace
```python
from datasets import load_dataset
dataset = load_dataset("AmazonScience/massive", "en-US", split='train')
print(dataset[0])
```
## Dataset Structure
### Data Instances
```json
{
"id": "0",
"locale": "fr-FR",
"partition": "test",
"scenario": "alarm",
"intent": "alarm_set",
"utt": "réveille-moi à cinq heures du matin cette semaine",
"annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]",
"worker_id": "22",
"slot_method": [
{ "slot": "time", "method": "translation" },
{ "slot": "date", "method": "translation" }
],
"judgments": [
{
"worker_id": "22",
"intent_score": 1,
"slots_score": 1,
"grammar_score": 4,
"spelling_score": 2,
"language_identification": "target"
},
{
"worker_id": "8",
"intent_score": 1,
"slots_score": 1,
"grammar_score": 4,
"spelling_score": 2,
"language_identification": "target"
},
{
"worker_id": "0",
"intent_score": 1,
"slots_score": 1,
"grammar_score": 4,
"spelling_score": 2,
"language_identification": "target"
}
]
}
```
### Data Fields
`id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization.
`locale`: is the language and country code accoring to ISO-639-1 and ISO-3166.
`partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp).
`scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance
`intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}`
`utt`: the raw utterance text without annotations
`annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]`
`worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales.
`slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification).
`judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker.
```plain
intent_score : "Does the sentence match the intent?"
0: No
1: Yes
2: It is a reasonable interpretation of the goal
slots_score : "Do all these terms match the categories in square brackets?"
0: No
1: Yes
2: There are no words in square brackets (utterance without a slot)
grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?"
0: Completely unnatural (nonsensical, cannot be understood at all)
1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language)
2: Some errors (the meaning can be understood but it doesn't sound natural in your language)
3: Good enough (easily understood and sounds almost natural in your language)
4: Perfect (sounds natural in your language)
spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error."
0: There are more than 2 spelling errors
1: There are 1-2 spelling errors
2: All words are spelled correctly
language_identification : "The following sentence contains words in the following languages (check all that apply)"
1: target
2: english
3: other
4: target & english
5: target & other
6: english & other
7: target & english & other
```
### Data Splits
|Language|Train|Dev|Test|
|:---:|:---:|:---:|:---:|
|af-ZA|11514|2033|2974|
|am-ET|11514|2033|2974|
|ar-SA|11514|2033|2974|
|az-AZ|11514|2033|2974|
|bn-BD|11514|2033|2974|
|ca-ES|11514|2033|2974|
|cy-GB|11514|2033|2974|
|da-DK|11514|2033|2974|
|de-DE|11514|2033|2974|
|el-GR|11514|2033|2974|
|en-US|11514|2033|2974|
|es-ES|11514|2033|2974|
|fa-IR|11514|2033|2974|
|fi-FI|11514|2033|2974|
|fr-FR|11514|2033|2974|
|he-IL|11514|2033|2974|
|hi-IN|11514|2033|2974|
|hu-HU|11514|2033|2974|
|hy-AM|11514|2033|2974|
|id-ID|11514|2033|2974|
|is-IS|11514|2033|2974|
|it-IT|11514|2033|2974|
|ja-JP|11514|2033|2974|
|jv-ID|11514|2033|2974|
|ka-GE|11514|2033|2974|
|km-KH|11514|2033|2974|
|kn-IN|11514|2033|2974|
|ko-KR|11514|2033|2974|
|lv-LV|11514|2033|2974|
|ml-IN|11514|2033|2974|
|mn-MN|11514|2033|2974|
|ms-MY|11514|2033|2974|
|my-MM|11514|2033|2974|
|nb-NO|11514|2033|2974|
|nl-NL|11514|2033|2974|
|pl-PL|11514|2033|2974|
|pt-PT|11514|2033|2974|
|ro-RO|11514|2033|2974|
|ru-RU|11514|2033|2974|
|sl-SL|11514|2033|2974|
|sq-AL|11514|2033|2974|
|sv-SE|11514|2033|2974|
|sw-KE|11514|2033|2974|
|ta-IN|11514|2033|2974|
|te-IN|11514|2033|2974|
|th-TH|11514|2033|2974|
|tl-PH|11514|2033|2974|
|tr-TR|11514|2033|2974|
|ur-PK|11514|2033|2974|
|vi-VN|11514|2033|2974|
|zh-CN|11514|2033|2974|
|zh-TW|11514|2033|2974|
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Additional Information
### Dataset Curators
__MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan.
__SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena.
__Hugging Face Upload and Integration__: Labrak Yanis (Not affiliated with the original corpus)
### Licensing Information
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### Citation Information
Please cite the following papers when using this dataset.
```latex
@misc{fitzgerald2022massive,
title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},
year={2022},
eprint={2204.08582},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{bastianelli-etal-2020-slurp,
title = "{SLURP}: A Spoken Language Understanding Resource Package",
author = "Bastianelli, Emanuele and
Vanzo, Andrea and
Swietojanski, Pawel and
Rieser, Verena",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.588",
doi = "10.18653/v1/2020.emnlp-main.588",
pages = "7252--7262",
abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp."
}
```
| AmazonScience/massive | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:af-ZA",
"multilinguality:am-ET",
"multilinguality:ar-SA",
"multilinguality:az-AZ",
"multilinguality:bn-BD",
"multilinguality:ca-ES",
"multilinguality:cy-GB",
"multilinguality:da-DK",
"multilinguality:de-DE",
"multilinguality:el-GR",
"multilinguality:en-US",
"multilinguality:es-ES",
"multilinguality:fa-IR",
"multilinguality:fi-FI",
"multilinguality:fr-FR",
"multilinguality:he-IL",
"multilinguality:hi-IN",
"multilinguality:hu-HU",
"multilinguality:hy-AM",
"multilinguality:id-ID",
"multilinguality:is-IS",
"multilinguality:it-IT",
"multilinguality:ja-JP",
"multilinguality:jv-ID",
"multilinguality:ka-GE",
"multilinguality:km-KH",
"multilinguality:kn-IN",
"multilinguality:ko-KR",
"multilinguality:lv-LV",
"multilinguality:ml-IN",
"multilinguality:mn-MN",
"multilinguality:ms-MY",
"multilinguality:my-MM",
"multilinguality:nb-NO",
"multilinguality:nl-NL",
"multilinguality:pl-PL",
"multilinguality:pt-PT",
"multilinguality:ro-RO",
"multilinguality:ru-RU",
"multilinguality:sl-SL",
"multilinguality:sq-AL",
"multilinguality:sv-SE",
"multilinguality:sw-KE",
"multilinguality:ta-IN",
"multilinguality:te-IN",
"multilinguality:th-TH",
"multilinguality:tl-PH",
"multilinguality:tr-TR",
"multilinguality:ur-PK",
"multilinguality:vi-VN",
"multilinguality:zh-CN",
"multilinguality:zh-TW",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:cc-by-4.0",
"natural-language-understanding",
"arxiv:2204.08582",
"region:us"
] | 2022-04-27T19:48:46+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "license": ["cc-by-4.0"], "multilinguality": ["af-ZA", "am-ET", "ar-SA", "az-AZ", "bn-BD", "ca-ES", "cy-GB", "da-DK", "de-DE", "el-GR", "en-US", "es-ES", "fa-IR", "fi-FI", "fr-FR", "he-IL", "hi-IN", "hu-HU", "hy-AM", "id-ID", "is-IS", "it-IT", "ja-JP", "jv-ID", "ka-GE", "km-KH", "kn-IN", "ko-KR", "lv-LV", "ml-IN", "mn-MN", "ms-MY", "my-MM", "nb-NO", "nl-NL", "pl-PL", "pt-PT", "ro-RO", "ru-RU", "sl-SL", "sq-AL", "sv-SE", "sw-KE", "ta-IN", "te-IN", "th-TH", "tl-PH", "tr-TR", "ur-PK", "vi-VN", "zh-CN", "zh-TW"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["intent-classification", "multi-class-classification"], "paperswithcode_id": "massive", "pretty_name": "MASSIVE", "language_bcp47": ["af-ZA", "am-ET", "ar-SA", "az-AZ", "bn-BD", "ca-ES", "cy-GB", "da-DK", "de-DE", "el-GR", "en-US", "es-ES", "fa-IR", "fi-FI", "fr-FR", "he-IL", "hi-IN", "hu-HU", "hy-AM", "id-ID", "is-IS", "it-IT", "ja-JP", "jv-ID", "ka-GE", "km-KH", "kn-IN", "ko-KR", "lv-LV", "ml-IN", "mn-MN", "ms-MY", "my-MM", "nb-NO", "nl-NL", "pl-PL", "pt-PT", "ro-RO", "ru-RU", "sl-SL", "sq-AL", "sv-SE", "sw-KE", "ta-IN", "te-IN", "th-TH", "tl-PH", "tr-TR", "ur-PK", "vi-VN", "zh-CN", "zh-TW"], "tags": ["natural-language-understanding"]} | 2022-11-16T15:44:51+00:00 |
3a14d4d277b7ad97c668740aa9d0affadbf3fe8d | # CodeParrot 🦜 Dataset Cleaned and filtered (validation)
## Dataset Description
A dataset of Python files from Github. It is a more filtered version of the validation split [codeparrot-clean-valid](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid) of [codeparrot-clean](https://huggingface.co/datasets/codeparrot/codeparrot-clean#codeparrot-%F0%9F%A6%9C-dataset-cleaned). The additional filters aim at detecting configuration and test files, as well as outlier files that are unlikely to help the model learn code. The first three filters are applied with a probability of 0.7:
- files with a mention of "test file" or "configuration file" or similar in the first 5 lines
- files with high occurence of the keywords "test " or "config"
- files without a mention of the keywords `def`, `for`, `while` and `class`
- files that use the assignment operator ```=``` less than 5 times
- files with ratio between number of characters and number of tokens after tokenization < 1.5 | codeparrot/codeparrot-valid-more-filtering | [
"region:us"
] | 2022-04-27T19:57:16+00:00 | {} | 2022-06-21T16:56:02+00:00 |
f6e726786732dbdb82a970091bb6d7ffe2251c80 | # CodeParrot 🦜 Dataset Cleaned and filtered (train)
## Dataset Description
A dataset of Python files from Github. It is a more filtered version of the train split [codeparrot-clean-train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-train) of [codeparrot-clean](https://huggingface.co/datasets/codeparrot/codeparrot-clean#codeparrot-%F0%9F%A6%9C-dataset-cleaned). The additional filters aim at detecting configuration and test files, as well as outlier files that are unlikely to help the model learn code. The first three filters are applied with a probability of 0.7:
- files with a mention of "test file" or "configuration file" or similar in the first 5 lines
- files with high occurence of the keywords "test " or "config"
- files without a mention of the keywords `def`, `for`, `while` and `class`
- files that use the assignment operator ```=``` less than 5 times
- files with ratio between number of characters and number of tokens after tokenization < 1.5
| codeparrot/codeparrot-train-more-filtering | [
"region:us"
] | 2022-04-27T21:28:47+00:00 | {} | 2022-06-21T16:54:51+00:00 |
c8e9269cd96a600bb340ee83ae45f46a02787a12 | # Dataset Card for Spanish IMDb Synopsis
## 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 Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
## Dataset Description
4969 movie synopsis from IMDb in spanish.
### Dataset Summary
[N/A]
### Languages
All descriptions are in spanish, the other fields have some mix of spanish and english.
## Dataset Structure
[N/A]
### Data Fields
- `description`: IMDb description for the movie (string), should be spanish
- `keywords`: IMDb keywords for the movie (string), mix of spanish and english
- `genre`: The genres of the movie (string), mix of spanish and english
- `year`: The year the movie was published (float)
- `name`: The name of the movie (string), mix of spanish and english
- `director`: The name of the main director in the movie, can be empty (string)
## Dataset Creation
[This kaggle dataset](https://www.kaggle.com/datasets/komalkhetlani/imdb-dataset) was used as a starting point. Then IMDb was scraped downloading the synopsis of the movies that have more than 5000 votes/reviews and those that did not have a synopsis available in Spanish were discarded. | mathigatti/spanish_imdb_synopsis | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:text2text-generation",
"annotations_creators:no-annotation",
"multilinguality:monolingual",
"language:es",
"license:apache-2.0",
"region:us"
] | 2022-04-27T23:54:42+00:00 | {"annotations_creators": ["no-annotation"], "language": ["es"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "task_categories": ["summarization", "text-generation", "text2text-generation"]} | 2022-10-25T09:12:53+00:00 |
12053f3054bf1cfe990b87e806b1f4ccde24e76b | chrisjay/crowd-speech-africa | [
"region:us"
] | 2022-04-28T05:14:44+00:00 | {"extra_gated_prompt": "You agree to not attempt to determine the identity of individuals in this dataset", "extra_gated_fields": {"Name": "text", "Affiliation": "text", "Email": "text", "I agree to not attempt to determine the identity of speakers in this dataset": "checkbox"}} | 2023-09-24T10:10:18+00:00 |
|
8ef331f75ebced42eceb7c5f53c47a3dafd8ef20 | hello
| TalTechNLP/VoxLingua107 | [
"license:cc-by-nc-4.0",
"region:us"
] | 2022-04-28T06:34:03+00:00 | {"license": "cc-by-nc-4.0"} | 2022-05-05T09:52:37+00:00 |
5d01e3f6a661d48e127ab5d7e3aaa0dc8331438a |
# Dataset Card for DanFEVER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://github.com/StrombergNLP/danfever](https://github.com/StrombergNLP/danfever)
- **Repository:** [https://stromberg.ai/publication/danfever/](https://stromberg.ai/publication/danfever/)
- **Paper:** [https://aclanthology.org/2021.nodalida-main.47/](https://aclanthology.org/2021.nodalida-main.47/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Leon Derczynski](mailto:leod@itu.dk)
- **Size of downloaded dataset files:** 2.82 MiB
- **Size of the generated dataset:** 2.80 MiB
- **Total amount of disk used:** 5.62 MiB
### Dataset Summary
We present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.
### Supported Tasks and Leaderboards
This dataset supports the FEVER task, but in Danish.
* PwC leaderboard: [Fact Verification on DanFEVER](https://paperswithcode.com/sota/fact-verification-on-danfever)
### Languages
This dataset is in Danish; the bcp47 is `da_DK`.
## Dataset Structure
### Data Instances
```
{
'id': '0',
'claim': 'Den 31. oktober 1920 opdagede Walter Baade kometen (944) Hidalgo i det ydre solsystem.',
'label': 0,
'evidence_extract': '(944) Hidalgo (oprindeligt midlertidigt navn: 1920 HZ) er en mørk småplanet med en diameter på ca. 50 km, der befinder sig i det ydre solsystem. Objektet blev opdaget den 31. oktober 1920 af Walter Baade. En asteroide (småplanet, planetoide) er et fast himmellegeme, hvis bane går rundt om Solen (eller en anden stjerne). Pr. 5. maj 2017 kendes mere end 729.626 asteroider og de fleste befinder sig i asteroidebæltet mellem Mars og Jupiter.',
'verifiable': 1,
'evidence': 'wiki_26366, wiki_12289',
'original_id': '1'
}
```
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
A dump of the Danish Wikipedia of 13 February 2020 was stored as well as the relevant articles from Den Store Danske (excerpts only, to comply with copyright laws). Two teams of two people independently sampled evidence, and created and annotated claims from these two sites.
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
The source language is from Wikipedia contributors editors and from dictionary contributors and editors.
### Annotations
#### Annotation process
Detailed in [this paper](http://www.derczynski.com/papers/danfever.pdf).
#### Who are the annotators?
The annotators are native Danish speakers and masters students of IT; two female, two male, ages 25-35.
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to enable construction of fact-checking systems in Danish. A system that succeeds at this may be able to identify questionable conclusions or inferences.
### Discussion of Biases
The data is drawn from relatively formal topics, and so may perform poorly outside these areas.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The data here is licensed CC-BY 4.0. If you use this data, you MUST state its origin.
### Citation Information
Refer to this work as:
> Nørregaard and Derczynski (2021). "DanFEVER: claim verification dataset for Danish", Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa).
Bibliographic reference:
````
@inproceedings{norregaard-derczynski-2021-danfever,
title = "{D}an{FEVER}: claim verification dataset for {D}anish",
author = "N{\o}rregaard, Jeppe and Derczynski, Leon",
booktitle = "Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)",
year = "2021",
publisher = {Link{\"o}ping University Electronic Press, Sweden},
url = "https://aclanthology.org/2021.nodalida-main.47",
pages = "422--428"
}
```
| strombergnlp/danfever | [
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"knowledge-verification",
"region:us"
] | 2022-04-28T08:17:29+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["fact-checking", "natural-language-inference"], "paperswithcode_id": "danfever", "pretty_name": "DanFEVER", "tags": ["knowledge-verification"]} | 2022-10-25T20:42:40+00:00 |
dad84ecc9f47c2d0701018018903a158bad71867 | JbIPS/stanford-dogs | [
"license:mit",
"region:us"
] | 2022-04-28T08:56:39+00:00 | {"license": "mit"} | 2022-04-28T08:56:39+00:00 |
|
d766cb8a7497d0d507d81f5f681a8d58deedf495 |
# Dataset Card for broad_twitter_corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
- **Repository:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
- **Paper:** [http://www.aclweb.org/anthology/C16-1111](http://www.aclweb.org/anthology/C16-1111)
- **Leaderboard:** [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
### Dataset Summary
This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.
See the paper, [Broad Twitter Corpus: A Diverse Named Entity Recognition Resource](http://www.aclweb.org/anthology/C16-1111), for details.
### Supported Tasks and Leaderboards
* Named Entity Recognition
* On PWC: [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
### Languages
English from UK, US, Australia, Canada, Ireland, New Zealand; `bcp47:en`
## Dataset Structure
### Data Instances
Feature |Count
---|---:
Documents |9 551
Tokens |165 739
Person entities |5 271
Location entities |3 114
Organization entities |3 732
### Data Fields
Each tweet contains an ID, a list of tokens, and a list of NER tags
- `id`: a `string` feature.
- `tokens`: a `list` of `strings`
- `ner_tags`: a `list` of class IDs (`int`s) representing the NER class:
```
0: O
1: B-PER
2: I-PER
3: B-ORG
4: I-ORG
5: B-LOC
6: I-LOC
```
### Data Splits
Section|Region|Collection period|Description|Annotators|Tweet count
---|---|---|---|---|---:
A | UK| 2012.01| General collection |Expert| 1000
B |UK |2012.01-02 |Non-directed tweets |Expert |2000
E |Global| 2014.07| Related to MH17 disaster| Crowd & expert |200
F |Stratified |2009-2014| Twitterati |Crowd & expert |2000
G |Stratified| 2011-2014| Mainstream news| Crowd & expert| 2351
H |Non-UK| 2014 |General collection |Crowd & expert |2000
The most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.
**Test**: Section F
**Development**: Section H (the paper says "second half of Section H" but ordinality could be ambiguous, so it all goes in. Bonne chance)
**Training**: everything else
## 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
Creative Commons Attribution 4.0 International (CC BY 4.0)
### Citation Information
```
@inproceedings{derczynski2016broad,
title={Broad twitter corpus: A diverse named entity recognition resource},
author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},
booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
pages={1169--1179},
year={2016}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
| strombergnlp/broad_twitter_corpus | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-04-28T08:58:09+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "broad-twitter-corpus", "pretty_name": "Broad Twitter Corpus"} | 2022-07-01T14:46:36+00:00 |
cc150b1a28983f4796ab486f6e1ef1d1047e523a |
# Dataset Card for "ipm-nel"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** []()
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [http://www.derczynski.com/papers/ner_single.pdf](http://www.derczynski.com/papers/ner_single.pdf)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 120 KB
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
This data is for the task of named entity recognition and linking/disambiguation over tweets. It comprises
the addition of an entity URI layer on top of an NER-annotated tweet dataset. The task is to detect entities
and then provide a correct link to them in DBpedia, thus disambiguating otherwise ambiguous entity surface
forms; for example, this means linking "Paris" to the correct instance of a city named that (e.g. Paris,
France vs. Paris, Texas).
The data concentrates on ten types of named entities: company, facility, geographic location, movie, musical
artist, person, product, sports team, TV show, and other.
The file is tab separated, in CoNLL format, with line breaks between tweets.
* Data preserves the tokenisation used in the Ritter datasets.
* PoS labels are not present for all tweets, but where they could be found in the Ritter data, they're given.
* In cases where a URI could not be agreed, or was not present in DBpedia, the linking URI is `NIL`.
See the paper, [Analysis of Named Entity Recognition and Linking for Tweets](http://www.derczynski.com/papers/ner_single.pdf) for a full description of the methodology.
### Supported Tasks and Leaderboards
* Dataset leaderboard on PWC: [Entity Linking on Derczynski](https://paperswithcode.com/sota/entity-linking-on-derczynski-1)
### Languages
English of unknown region (`bcp47:en`)
## Dataset Structure
### Data Instances
#### ipm_nel
- **Size of downloaded dataset files:** 120 KB
- **Size of the generated dataset:**
- **Total amount of disk used:**
An example of 'train' looks as follows.
```
{
'id': '0',
'tokens': ['#Astros', 'lineup', 'for', 'tonight', '.', 'Keppinger', 'sits', ',', 'Downs', 'plays', '2B', ',', 'CJ', 'bats', '5th', '.', '@alysonfooter', 'http://bit.ly/bHvgCS'],
'ner_tags': [9, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 0, 0],
'uris': "['http://dbpedia.org/resource/Houston_Astros', '', '', '', '', 'http://dbpedia.org/resource/Jeff_Keppinger', '', '', 'http://dbpedia.org/resource/Brodie_Downs', '', '', '', 'NIL', '', '', '', '', '']"
}
```
### Data Fields
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
- `uris`: a `list` of URIs (`string`) that disambiguate entities. Set to `NIL` when an entity has no DBpedia entry, or blank for outside-of-entity tokens.
### Data Splits
| name |train|
|---------|----:|
|ipm_nel|183 sentences|
## Dataset Creation
### Curation Rationale
To gather a social media benchmark for named entity linking that is sufficiently different from newswire data.
### Source Data
#### Initial Data Collection and Normalization
The data is partly harvested from that distributed by [Ritter / Named Entity Recognition in Tweets: An Experimental Study](https://aclanthology.org/D11-1141/),
and partly taken from Twitter by the authors.
#### Who are the source language producers?
English-speaking Twitter users, between October 2011 and September 2013
### Annotations
#### Annotation process
The authors were allocated documents and marked them for named entities (where these were not already present) and then attempted to find
the best-fitting DBpedia entry for each entity found. Each entity mention was labelled by a random set of three volunteers.
The annotation task was mediated using Crowdflower (Biewald, 2012). Our interface design was to show each volunteer the text of the tweet, any URL links contained
therein, and a set of candidate targets from DBpedia. The volunteers were encouraged to click on the URL links from the
tweet, to gain addition context and thus ensure that the correct DBpedia URI is chosen by them. Candidate entities were
shown in random order, using the text from the corresponding DBpedia abstracts (where available) or the actual DBpedia
URI otherwise. In addition, the options ‘‘none of the above’’, ‘‘not an entity’’ and ‘‘cannot decide’’ were added, to allow the
volunteers to indicate that this entity mention has no corresponding DBpedia URI (none of the above), the highlighted text
is not an entity, or that the tweet text (and any links, if available) did not provide sufficient information to reliably disambiguate the entity mention.
#### Who are the annotators?
The annotators are 10 volunteer NLP researchers, from the authors and the authors' institutions.
### Personal and Sensitive Information
The data was public at the time of collection. User names are preserved.
## Considerations for Using the Data
### Social Impact of Dataset
There's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.
### Discussion of Biases
The data is annotated by NLP researchers; we know that this group has high agreement but low recall on English twitter text [C16-1111](https://aclanthology.org/C16-1111/).
### Other Known Limitations
The above limitations apply.
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0. You must
acknowledge the author if you use this data, but apart from that, you're quite
free to do most things. See https://creativecommons.org/licenses/by/4.0/legalcode .
### Citation Information
```
@article{derczynski2015analysis,
title={Analysis of named entity recognition and linking for tweets},
author={Derczynski, Leon and Maynard, Diana and Rizzo, Giuseppe and Van Erp, Marieke and Gorrell, Genevieve and Troncy, Rapha{\"e}l and Petrak, Johann and Bontcheva, Kalina},
journal={Information Processing \& Management},
volume={51},
number={2},
pages={32--49},
year={2015},
publisher={Elsevier}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
| strombergnlp/ipm_nel | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"named-entity-linking",
"region:us"
] | 2022-04-28T09:06:10+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "ipm-nel", "pretty_name": "IPM NEL (Derczynski)", "tags": ["named-entity-linking"]} | 2022-10-25T20:41:26+00:00 |
5e511fb803405e3b2454c77ba8928fe47557017e |
# Dataset Card for "shaj"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [https://figshare.com/articles/dataset/SHAJ_Albanian_hate_speech_abusive_language/19333298/1](https://figshare.com/articles/dataset/SHAJ_Albanian_hate_speech_abusive_language/19333298/1)
- **Paper:** [https://arxiv.org/abs/2107.13592](https://arxiv.org/abs/2107.13592)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 769.21 KiB
- **Size of the generated dataset:** 1.06 MiB
- **Total amount of disk used:** 1.85 MiB
### Dataset Summary
This is an abusive/offensive language detection dataset for Albanian. The data is formatted
following the OffensEval convention, with three tasks:
* Subtask A: Offensive (OFF) or not (NOT)
* Subtask B: Untargeted (UNT) or targeted insult (TIN)
* Subtask C: Type of target: individual (IND), group (GRP), or other (OTH)
Notes on the above:
* The subtask A field should always be filled.
* The subtask B field should only be filled if there's "offensive" (OFF) in A.
* The subtask C field should only be filled if there's "targeted" (TIN) in B.
The dataset name is a backronym, also standing for "Spoken Hate in the Albanian Jargon"
See the paper [https://arxiv.org/abs/2107.13592](https://arxiv.org/abs/2107.13592) for full details.
### Supported Tasks and Leaderboards
* Task A leaderboard at [paperswithcode.com/sota/hate-speech-detection-on-shaj](https://paperswithcode.com/sota/hate-speech-detection-on-shaj)
### Languages
Albanian (`bcp47:sq-AL`)
## Dataset Structure
### Data Instances
#### shaj
- **Size of downloaded dataset files:** 769.21 KiB
- **Size of the generated dataset:** 1.06 MiB
- **Total amount of disk used:** 1.85 MiB
An example of 'train' looks as follows.
```
{
'id': '0',
'text': 'PLACEHOLDER TEXT',
'subtask_a': 1,
'subtask_b': 0,
'subtask_c': 0
}
```
### Data Fields
- `id`: a `string` feature.
- `text`: a `string`.
- `subtask_a`: whether or not the instance is offensive; `0: OFF, 1: NOT`
- `subtask_b`: whether an offensive instance is a targeted insult; `0: TIN, 1: UNT, 2: not applicable`
- `subtask_c`: what a targeted insult is aimed at; `0: IND, 1: GRP, 2: OTH, 3: not applicable`
### Data Splits
| name |train|
|---------|----:|
|shaj|11874 sentences|
## Dataset Creation
### Curation Rationale
Collecting data for enabling offensive speech detection in Albanian
### Source Data
#### Initial Data Collection and Normalization
The text is scraped from comments on popular Albanian YouTube and Instagram accounts.
An extended discussion is given in the paper in section 3.2.
#### Who are the source language producers?
People who comment on a selection of high-activity Albanian instagram and youtube profiles.
### Annotations
#### Annotation process
The annotation scheme was taken from OffensEval 2019 and applied by two native speaker authors of the paper as well as their friends and family.
#### Who are the annotators?
Albanian native speakers, male and female, aged 20-60.
### Personal and Sensitive Information
The data was public at the time of collection. No PII removal has been performed.
## Considerations for Using the Data
### Social Impact of Dataset
The data definitely contains abusive language.
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
### Citation Information
```
@article{nurce2021detecting,
title={Detecting Abusive Albanian},
author={Nurce, Erida and Keci, Jorgel and Derczynski, Leon},
journal={arXiv preprint arXiv:2107.13592},
year={2021}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
| strombergnlp/shaj | [
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"arxiv:2107.13592",
"doi:10.57967/hf/0514",
"region:us"
] | 2022-04-28T09:07:18+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text_classification"], "task_ids": ["hate-speech-detection", "text-classification-other-hate-speech-detection"], "paperswithcode_id": "shaj", "pretty_name": "SHAJ", "languages": ["sq", "sq-AL"], "licenses": ["cc-by-4.0"], "extra_gated_prompt": "Warning: this repository contains harmful content (abusive language, hate speech)."} | 2022-06-14T13:03:37+00:00 |
1075212523ac84e51b0fe6bc41fef4cd2bf695cc |
# Dataset Card for "dkstance / DAST"
## 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://stromberg.ai/publication/jointrumourstanceandveracity/](https://stromberg.ai/publication/jointrumourstanceandveracity/)
- **Repository:** [https://figshare.com/articles/dataset/Danish_stance-annotated_Reddit_dataset/8217137](https://figshare.com/articles/dataset/Danish_stance-annotated_Reddit_dataset/8217137)
- **Paper:** [https://aclanthology.org/W19-6122/](https://aclanthology.org/W19-6122/)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
This is an SDQC stance-annotated Reddit dataset for the Danish language generated within a thesis project. The dataset consists of over 5000 comments structured as comment trees and linked to 33 source posts.
The dataset is applicable for supervised stance classification and rumour veracity prediction.
### Supported Tasks and Leaderboards
* Stance prediction
### Languages
## Dataset Structure
### Data Instances
#### DAST / dkstance
- **Size of downloaded dataset files:** 4.72 MiB
- **Size of the generated dataset:** 3.69 MiB
- **Total amount of disk used:** 8.41 MiB
An example of 'train' looks as follows.
```
{
'id': '1',
'native_id': 'ebwjq5z',
'text': 'Med de udfordringer som daginstitutionerne har med normeringer, og økonomi i det hele taget, synes jeg det er en vanvittig beslutning at prioritere skattebetalt vegansk kost i daginstitutionerne. Brug dog pengene på noget mere personale, og lad folk selv betale for deres individuelle kostønsker.',
'parent_id': 'a6o3us',
'parent_text': 'Mai Mercado om mad i daginstitutioner: Sund kost rimer ikke på veganer-mad',
'parent_stance': 0,
'source_id': 'a6o3us',
'source_text': 'Mai Mercado om mad i daginstitutioner: Sund kost rimer ikke på veganer-mad',
'source_stance': 0
}
```
### Data Fields
- `id`: a `string` feature.
- `native_id`: a `string` feature representing the native ID of the entry.
- `text`: a `string` of the comment text in which stance is annotated.
- `parent_id`: the `native_id` of this comment's parent.
- `parent_text`: a `string` of the parent comment's text.
- `parent_stance`: the label of the stance in the comment towards its parent comment.
```
0: "Supporting",
1: "Denying",
2: "Querying",
3: "Commenting",
```
- `source_id`: the `native_id` of this comment's source / post.
- `source_text`: a `string` of the source / post text.
- `source_stance`: the label of the stance in the comment towards the original source post.
```
0: "Supporting",
1: "Denying",
2: "Querying",
3: "Commenting",
```
### Data Splits
| name |size|
|---------|----:|
|train|3122|
|validation|1066|
|test|1060|
These splits are specified after the original reserach was reported. The splits add an extra level of rigour, in that no source posts' comment tree is spread over more than one partition.
## Dataset Creation
### Curation Rationale
Comments around rumourous claims to enable rumour and stance analysis in Danish
### Source Data
#### Initial Data Collection and Normalization
The data is from Reddit posts that relate to one of a specific set of news stories; these stories are enumerated in the paper.
#### Who are the source language producers?
Danish-speaking Twitter users.
### Annotations
#### Annotation process
There was multi-user annotation process mediated through a purpose-built interface for annotating stance in Reddit threads.
#### Who are the annotators?
* Age: 20-30.
* Gender: male.
* Race/ethnicity: white northern European.
* Native language: Danish.
* Socioeconomic status: higher education student.
### Personal and Sensitive Information
The data was public at the time of collection. User names are not preserved.
## Considerations for Using the Data
### Social Impact of Dataset
There's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.
### Discussion of Biases
The source of the text has a strong demographic bias, being mostly young white men who are vocal their opinions. This constrains both the styles of language and discussion contained in the data, as well as the topics discussed and viewpoints held.
### Other Known Limitations
The above limitations apply.
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
An NLP data statement is included in the paper describing the work, [https://aclanthology.org/W19-6122.pdf](https://aclanthology.org/W19-6122.pdf)
### Citation Information
```
@inproceedings{lillie-etal-2019-joint,
title = "Joint Rumour Stance and Veracity Prediction",
author = "Lillie, Anders Edelbo and
Middelboe, Emil Refsgaard and
Derczynski, Leon",
booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
month = sep # "{--}" # oct,
year = "2019",
address = "Turku, Finland",
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/W19-6122",
pages = "208--221",
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
| strombergnlp/dkstance | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"stance-detection",
"region:us"
] | 2022-04-28T09:07:39+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["fact-checking"], "paperswithcode_id": "dast", "pretty_name": "DAST", "extra_gated_prompt": "Warning: the data in this repository contains harmful content (misinformative claims).", "tags": ["stance-detection"]} | 2022-10-25T20:45:42+00:00 |
d617a5df1b7ec95b1b290926e2d1f20c55d2c0b9 |
# Dataset Card for "polstance"
## 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://stromberg.ai/publication/politicalstanceindanish/](https://stromberg.ai/publication/politicalstanceindanish/)
- **Repository:** [https://github.com/StrombergNLP/Political-Stance-in-Danish/](https://github.com/StrombergNLP/Political-Stance-in-Danish/)
- **Paper:** [https://aclanthology.org/W19-6121/](https://aclanthology.org/W19-6121/)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 548 KB
- **Size of the generated dataset:** 222 KB
- **Total amount of disk used:** 770 KB
### Dataset Summary
Political stance in Danish. Examples represent statements by
politicians and are annotated for, against, or neutral to a given topic/article.
### Supported Tasks and Leaderboards
*
### Languages
Danish, bcp47: `da-DK`
## Dataset Structure
### Data Instances
#### polstance
An example of 'train' looks as follows.
```
{
'id': '0',
'topic': 'integration',
'quote': 'Der kunne jeg godt tænke mig, at der stod mere eksplicit, at de (landene, red.) skal bekæmpe menneskesmuglere og tage imod deres egne borgere',
'label': 2,
'quoteID': '516',
'party': 'Det Konservative Folkeparti',
'politician': 'Naser Khader',
}
```
### Data Fields
- `id`: a `string` feature.
- `topic`: a `string` expressing a topic.
- `quote`: a `string` to be classified for its stance to the topic.
- `label`: a class label representing the stance the text expresses towards the target. Full tagset with indices:
```
0: "against",
1: "neutral",
2: "for",
```
- `quoteID`: a `string` of the internal quote ID.
- `party`: a `string` describing the party affiliation of the quote utterer at the time of utterance.
- `politician`: a `string` naming the politician who uttered the quote.
### Data Splits
| name |train|
|---------|----:|
|polstance|900 sentences|
## Dataset Creation
### Curation Rationale
Collection of quotes from politicians to allow detecting how political quotes orient to issues.
### Source Data
#### Initial Data Collection and Normalization
The data is taken from proceedings of the Danish parliament, the Folketing - [ft.dk](https://ft.dk).
#### Who are the source language producers?
Danish polticians
### Annotations
#### Annotation process
Annotators labelled comments for being against, neutral, or for a specified topic
#### Who are the annotators?
Danish native speakers, 20s, male, studying Software Design.
### Personal and Sensitive Information
The data was public at the time of collection and will remain open public record by law in Denmark.
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
The above limitations apply.
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
### Citation Information
```
@inproceedings{lehmann2019political,
title={Political Stance in Danish},
author={Lehmann, Rasmus and Derczynski, Leon},
booktitle={Proceedings of the 22nd Nordic Conference on Computational Linguistics},
pages={197--207},
year={2019}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
| strombergnlp/polstance | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"stance-detection",
"region:us"
] | 2022-04-28T09:08:13+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"], "paperswithcode_id": "polstance", "pretty_name": "Political Stance for Danish", "tags": ["stance-detection"]} | 2022-10-25T20:42:18+00:00 |
8ad62edaaf487414e527d3f59edd6f6b52778b41 |
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/StrombergNLP/bornholmsk
- **Repository:** https://github.com/StrombergNLP/bornholmsk
- **Paper:** https://aclanthology.org/W19-6138/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
### Dataset Summary
This corpus introduces language processing resources and tools for Bornholmsk, a language spoken on the island of Bornholm, with roots in Danish and closely related to Scanian.
Sammenfattnijng på borrijnholmst: Dæjnna artikkelijn introduserer natursprågsresurser å varktoi for borrijnholmst, ed språg a dær snakkes på ön Borrijnholm me rødder i danst å i nær familia me skånst.
For more details, see the paper [Bornholmsk Natural Language Processing: Resources and Tools](https://aclanthology.org/W19-6138/).
### Supported Tasks and Leaderboards
*
### Languages
Bornholmsk, a language variant of Danish spoken on the island of Bornholm. bcp47: `da-bornholm`
## Dataset Structure
### Data Instances
13169 lines, 175 167 words, 801 KB
### Data Fields
`id`: the sentence ID, `int`
`text`: the Bornholmsk text, `string`
### Data Splits
Monolithic
## Dataset Creation
### Curation Rationale
To gather as much digital Bornholmsk together as possible
### Source Data
#### Initial Data Collection and Normalization
From many places - see paper for details. Sources include poems, songs, translations from Danish, folk stories, dictionary entries.
#### Who are the source language producers?
Native speakers of Bornholmsk who have produced works in their native language, or translated them to Danish. Much of the data is the result of a community of Bornholmsk speakers volunteering their time across the island in an effort to capture this endangered language.
### Annotations
#### Annotation process
No annotations
#### Who are the annotators?
No annotations
### Personal and Sensitive Information
Unknown, but low risk of presence, given the source material
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to capture Bornholmsk digitally and provide a way for NLP systems to interact with it, and perhaps even spark interest in dealing with the language.
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
This collection of Bornholmsk is curated by Leon Derczynski and Alex Speed Kjeldsen
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
```
@inproceedings{derczynski-kjeldsen-2019-bornholmsk,
title = "Bornholmsk Natural Language Processing: Resources and Tools",
author = "Derczynski, Leon and
Kjeldsen, Alex Speed",
booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
month = sep # "{--}" # oct,
year = "2019",
address = "Turku, Finland",
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/W19-6138",
pages = "338--344",
}
``` | strombergnlp/bornholmsk | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"region:us"
] | 2022-04-28T09:08:37+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "language_bcp47": ["da", "da-bornholm"]} | 2022-10-25T20:40:56+00:00 |
12ff587afc996106440872be6b3656218fad0e82 |
# Dataset Card for "twitter-pos-vcb"
## 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://gate.ac.uk/wiki/twitter-postagger.html](https://gate.ac.uk/wiki/twitter-postagger.html)
- **Repository:** [https://github.com/GateNLP/gateplugin-Twitter](https://github.com/GateNLP/gateplugin-Twitter)
- **Paper:** [https://aclanthology.org/R13-1026.pdf](https://aclanthology.org/R13-1026.pdf)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 4.51 MiB
- **Size of the generated dataset:** 26.88 MB
- **Total amount of disk used:** 31.39 MB
### Dataset Summary
Part-of-speech information is basic NLP task. However, Twitter text
is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style.
This data is the vote-constrained bootstrapped data generate to support state-of-the-art results.
The data is about 1.5 million English tweets annotated for part-of-speech using Ritter's extension of the PTB tagset.
The tweets are from 2012 and 2013, tokenized using the GATE tokenizer and tagged
jointly using the CMU ARK tagger and Ritter's T-POS tagger. Only when both these taggers' outputs
are completely compatible over a whole tweet, is that tweet added to the dataset.
This data is recommend for use a training data **only**, and not evaluation data.
For more details see https://gate.ac.uk/wiki/twitter-postagger.html and https://aclanthology.org/R13-1026.pdf
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
English, non-region-specific. `bcp47:en`
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### twitter_pos_vcb
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
```
### Data Splits
| name |tokens|sentences|
|---------|----:|---------:|
|twitter-pos-vcb|1 543 126| 159 492|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
Creative Commons Attribution 4.0 (CC-BY)
### Citation Information
```
@inproceedings{derczynski2013twitter,
title={Twitter part-of-speech tagging for all: Overcoming sparse and noisy data},
author={Derczynski, Leon and Ritter, Alan and Clark, Sam and Bontcheva, Kalina},
booktitle={Proceedings of the international conference recent advances in natural language processing ranlp 2013},
pages={198--206},
year={2013}
}
```
### Contributions
Author uploaded ([@leondz](https://github.com/leondz)) | strombergnlp/twitter_pos_vcb | [
"task_categories:token-classification",
"task_ids:part-of-speech",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-04-28T09:10:59+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["part-of-speech"], "paperswithcode_id": "twitter-pos-vcb", "pretty_name": "Twitter PoS VCB"} | 2022-10-25T20:42:56+00:00 |
9cd7629e8632e2bf2185a7ae2309b8333248d57e |
# Dataset Card for "zulu-stance"
## 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://arxiv.org/abs/2205.03153](https://arxiv.org/abs/2205.03153)
- **Repository:**
- **Paper:** [https://arxiv.org/pdf/2205.03153](https://arxiv.org/pdf/2205.03153)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 212.54 KiB
- **Size of the generated dataset:** 186.76 KiB
- **Total amount of disk used:** 399.30KiB
### Dataset Summary
This is a stance detection dataset in the Zulu language. The data is translated to Zulu by Zulu native speakers, from English source texts.
Our paper aims at utilizing this progress made for English to transfers that knowledge into other languages, which is a non-trivial task due to the domain gap between English and the target languages. We propose a black-box non-intrusive method that utilizes techniques from Domain Adaptation to reduce the domain gap, without requiring any human expertise in the target language, by leveraging low-quality data in both a supervised and unsupervised manner. This allows us to rapidly achieve similar results for stance detection for the Zulu language, the target language in this work, as are found for English. A natively-translated dataset is used for evaluation of domain transfer.
### Supported Tasks and Leaderboards
*
### Languages
Zulu (`bcp47:zu`)
## Dataset Structure
### Data Instances
#### zulu_stance
- **Size of downloaded dataset files:** 212.54 KiB
- **Size of the generated dataset:** 186.76 KiB
- **Total amount of disk used:** 399.30KiB
An example of 'train' looks as follows.
```
{
'id': '0',
'text': 'ubukhulu be-islam buba sobala lapho i-smartphone ifaka i-ramayana njengo-ramadan. #semst',
'target': 'Atheism',
'stance': 1}
```
### Data Fields
- `id`: a `string` feature.
- `text`: a `string` expressing a stance.
- `target`: a `string` of the target/topic annotated here.
- `stance`: a class label representing the stance the text expresses towards the target. Full tagset with indices:
```
0: "FAVOR",
1: "AGAINST",
2: "NONE",
```
### Data Splits
| name |train|
|---------|----:|
|zulu_stance|1343 sentences|
## Dataset Creation
### Curation Rationale
To enable stance detection in Zulu and also to measure domain transfer in translation
### Source Data
#### Initial Data Collection and Normalization
The original data is taken from [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/),
and then translated manually to Zulu.
#### Who are the source language producers?
English-speaking Twitter users.
### Annotations
#### Annotation process
See [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/); the annotations are taken from there.
#### Who are the annotators?
See [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/); the annotations are taken from there.
### Personal and Sensitive Information
The data was public at the time of collection. User names are preserved.
## Considerations for Using the Data
### Social Impact of Dataset
There's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.
### Discussion of Biases
While the data is in Zulu, the source text is not from or about Zulu-speakers, and so still expresses the social biases and topics found in English-speaking Twitter users. Further, some of the topics are USA-specific. The sentiments and ideas in this dataset do not represent Zulu speakers.
### Other Known Limitations
The above limitations apply.
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
### Citation Information
```
@inproceedings{dlamini_zulu_stance,
title={Bridging the Domain Gap for Stance Detection for the Zulu language},
author={Dlamini, Gcinizwe and Bekkouch, Imad Eddine Ibrahim and Khan, Adil and Derczynski, Leon},
booktitle={Proceedings of IEEE IntelliSys},
year={2022}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
| strombergnlp/zulu_stance | [
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zu",
"license:cc-by-4.0",
"stance-detection",
"arxiv:2205.03153",
"region:us"
] | 2022-04-28T09:24:38+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["zu"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["fact-checking", "sentiment-classification"], "paperswithcode_id": "zulu-stance", "pretty_name": "ZUstance", "tags": ["stance-detection"]} | 2022-10-25T20:46:14+00:00 |
00e360ccbfa82f47287311387af1011f28f7e667 | You should know how to use it:)
Just in case, you can email me [mingzhe at nus.edu.sg] if you need any help. | Elfsong/clef_data | [
"region:us"
] | 2022-04-28T10:31:27+00:00 | {} | 2022-08-29T04:41:54+00:00 |
3fc0666c45d46d03a9bcd43f5e887dda2727328e |
Adapted from https://github.com/nicholas-leonard/word2vec | tomasmcz/word2vec_analogy | [
"license:apache-2.0",
"region:us"
] | 2022-04-28T10:58:50+00:00 | {"license": "apache-2.0"} | 2022-04-28T13:07:56+00:00 |
d4b9a7de7eeea7fe4b1e43186b2e9d2b014779b8 | Zaratahir123/23100065 | [
"license:mit",
"region:us"
] | 2022-04-28T14:57:45+00:00 | {"license": "mit"} | 2022-04-28T15:05:20+00:00 |
|
a1f29ad15db2594f69420a2abc845cc0bd09a311 |
Possibly a placeholder dataset for the original here: https://huggingface.co/datasets/bigscience-catalogue-data/bias-shades
# Data Statement for SHADES
> **How to use this document:**
> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.
> For full details, the best source is the original Data Statements paper, here: https://www.aclweb.org/anthology/Q18-1041/ .
> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as "DATASTATEMENT.md". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.
> Only blockquoted content should be deleted; the final about statement should be left intact.
Data set name: Bias-Shades
Citation (if available): TODO.
Data set developer(s): This dataset was compiled by dozens of research scientists through the BigScience open science collaboration. Collaborators, representing numerous cultures and languages, joined the project of their own volition.
Data statement author(s): Shayne Longpre, Aurélie Névéol, Shanya Sharma[Add name here if you add/edit the data statement :)].
Others who contributed to this document: N/A
License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).
## A. CURATION RATIONALE
> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.
This dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted. An initial set of sentences was inferred from stereotypes expressed in the crowS-pairs data set(Nangia et al.). Native speakers first crafted templates for sentences expressing a stereotype. These templates are marked for gender and plurality of the target nouns, so the template can be reused by substituting different targets. Next, the template-target noun pair combinations were annotated for the veracity/reliability of the expressed stereotype. The resulting sentences express common and less common stereotypes in a variety of cultures and languages.
## B. LANGUAGE VARIETY/VARIETIES
> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., "English as spoken in Palo Alto, California", or "Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin").
* BCP-47 language tags: en-US, fr-FR, hi-IN, es-DO, ar-LY, ru-RU, de-DE, nl-NL, ta-IN.
* Language variety description: English spoken by native speakers of the United States, native French people from metropolitan France, native Hindi and Tamil speakers from India, Spanish speakers from the Dominican Republic, Arabic speakers from Libya, Russian speakers from Russia, German speakers from Germany, and Dutch speakers from the Netherlands.
## C. CONTRIBUTOR DEMOGRAPHIC
> ## C. SPEAKER DEMOGRAPHIC
> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include:
Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Listed below.
Speakers:
* [ADD YOURSELF!]
* Shayne Longpre: English-speaking, male, 28 years old, culturally Canadian.
* Aurélie Névéol: French (native), English and Spanish speaking, female, 44 years old, culturally French (also familiar with American culture)
* Shanya Sharma: Hindi(native), English speaking, female, 24 years old, culturally Indian
* Margaret Mitchell: English, female, mid-30s, U.S.A.
* Maraim Masoud: Arabic, English Speaking female.
## D. ANNOTATOR DEMOGRAPHIC
> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:
Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Speaker and annotator contributors listed in section C.
## E. SPEECH SITUATION
N/A
## F. TEXT CHARACTERISTICS
> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.
Collected data is a collection of offensive stereotyped statements in numerous languages and cultures. They might be upsetting and/or offensive.
Along with these stereotyped statements are annotation judgements of how prevalent/real the expressed stereotypes are in the real world. Some statements were created from templates with substituted target nouns, and therefore may express an uncommon or unlikely stereotype.
## G. RECORDING QUALITY
N/A
## H. OTHER
> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.
## I. PROVENANCE APPENDIX
This initiative is part of the BigScience Workshop: https://bigscience.huggingface.co/.
## About this document
A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.
Data Statements are from the University of Washington. Contact: [datastatements@uw.edu](mailto:datastatements@uw.edu). This document template is licensed as [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
This version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the [2020 LREC workshop on Data Statements](https://sites.google.com/uw.edu/data-statements-for-nlp/), by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski. | BigScienceBiasEval/bias-shades | [
"language:ar",
"language:en",
"language:fr",
"language:de",
"language:hi",
"language:ru",
"language:es",
"language:ta",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-04-28T15:46:11+00:00 | {"language": ["ar", "en", "fr", "de", "hi", "ru", "es", "ta"], "license": "cc-by-sa-4.0"} | 2024-01-14T11:45:29+00:00 |
f6eb365a04aacbd3b1055cf1cfea9edf1d6355f7 |
# Dataset Card for sci-definition
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/talaugust/definition-complexity
- **Repository:** https://github.com/talaugust/definition-complexity
- **Paper:** https://openreview.net/forum?id=S8x2x5EpWj
- **Point of Contact:** Tal August (taugust@cs.washington.edu)
### Dataset Summary
This is a dataset of scientific and medical definitions. They are drawn from two sources:
(MedQuAD)[https://github.com/abachaa/MedQuAD]
(Wikipedia Science Glossaries)[https://en.wikipedia.org/wiki/Category:Glossaries_of_science]
For both sources, all terms and definitions are formatted as "What is (are) X?" or "Do you have more information about X?" with the answer being the definition of X.
### Supported Tasks and Leaderboards
The task this dataset was created for was generating definitions of scientific terms. It has additionally be used to vary the complexity of generating definitions. Both versions of this task are described in the original paper.
### Languages
English
## Dataset Structure
### Data Instances
{
"question": "What is (are) complement component 2 deficiency?",
"category": "medquad",
"first_sentence": "Complement component 2 deficiency is a disorder that causes the immune system to malfunction, resulting in a form of immunodeficiency.",
"support_doc_sparse_s2orc":"The occurrence of membranoproliferative glomerulonephritis in a 13 year old boy with inherited complete deficiency of the second component of complement (C2) is described here....<P>...."
}
### Data Fields
question: The question containing the term
category: either medquad or wikipedia
first_sentence: The answer (or definition)
support_doc_sparse_s2orc: the support document, made up of 10 journal abstracts related to the term. Journal abstracts are drawn from S2ORC (https://github.com/allenai/s2orc) using ElasticSearch.
### Data Splits
Splits are in a test, train, and dev. As a note, the original results reported in the paper on generating definitions was on the dev set and training was done on a split of the train set. The original test set was reserved for evaluating complexity control.
## Dataset Creation
### Curation Rationale
We collected from these two sources because they contain expert-written and factually correct information about scientific and medical terms. For the MedQuAD data, we filter for questions of the form: What is (are) X? or Do you have more information on X? We then format the Wikipedia science glossaries in the same form ("What is (are) X? ).
### Source Data
#### Initial Data Collection and Normalization
The data was collected by filtering the original MedQuAD dataset and drawing on Wikpiedia science glossaries. This occurred between September 2020-March 2021.
#### Who are the source language producers?
NIH website (MedQuAD), and Wikpedia editors
### Annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
The goal of this dataset is to enable a wider audience of readers to understand and engage with scientific writing. A risk, though, is that such attempts might instead widen the gap to accessing scientific information.
### Discussion of Biases
The texts in the datasets we train our models on are in General or Academic American English. Many people, especially those who have been historically underrepresented in STEM disciplines and medicine, may not be comfortable with this dialect of English. This risks further alienating the readers we hope to serve.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Please refer to the (MedQuAD)[https://github.com/abachaa/MedQuAD] license
### Citation Information
@inproceedings{august-2022-definition-complexity,
title={Generating Scientific Definitions with Controllable Complexity},
author={Tal August, Katharina Reinecke, and Noah A. Smith},
booktitle={ACL},
year={2022}
} | talaugust/sci-definition | [
"region:us"
] | 2022-04-28T17:46:51+00:00 | {} | 2022-05-17T21:10:49+00:00 |
a4e4877f5ae4a8df754d2da61fd9b4f71dc3b6c4 | smallv0221/dd | [
"license:apache-2.0",
"region:us"
] | 2022-04-29T03:34:50+00:00 | {"license": "apache-2.0"} | 2022-04-29T03:34:50+00:00 |
|
96349b9bd7536aac67f892f5cf36197cd49ea722 | # Dataset Card for FactRuEval-2016
## Dataset Description
- **Point of Contact:** [Guskov Sergey](https://gusevski.com)
### Dataset Summary
Evaluation of [Named Entity Recognition](https://www.dialog-21.ru/media/3430/starostinaetal.pdf) and Fact Extraction Systems for Russian.
### Supported Tasks and Leaderboards
For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
- `token-classification`: The dataset can be used to train a model for [NER], which consists in [Token Classification]. Success on this task is typically measured by achieving a *high/low* [metric name](https://huggingface.co/metrics/metric_name). The ([model name](https://huggingface.co/model_name) or [model class](https://huggingface.co/transformers/model_doc/model_class.html)) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at [leaderboard url]() and ranks models based on [metric name](https://huggingface.co/metrics/metric_name) while also reporting [other metric name](https://huggingface.co/metrics/other_metric_name).
### Languages
RU.
## Dataset Structure
### Data Instances
Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.
```
{
'data': [{'id':'', 'tokens':[], 'ner_tags':[]},...],
...
}
```
Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.
### Data Fields
List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
- `id`: order id
- `tokens`: list of tokens
- `ner_tags`: list of ner tags
### Data Splits
Describe and name the splits in the dataset if there are more than one.
Describe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.
Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Sentences | | | |
| Average Sentence Length | | | |
## Dataset Creation
### Curation Rationale
What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?
### Source Data
This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
#### Initial Data Collection and Normalization
Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
#### Who are the source language producers?
State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.
If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
Describe other people represented or mentioned in the data. Where possible, link to references for the information.
### Annotations
If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
#### Annotation process
If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
#### Who are the annotators?
If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.
Describe the people or systems who originally created the annotations and their selection criteria if applicable.
If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
### Personal and Sensitive Information
State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).
State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).
If efforts were made to anonymize the data, describe the anonymization process.
## Considerations for Using the Data
### Social Impact of Dataset
Please discuss some of the ways you believe the use of this dataset will impact society.
The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.
Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.
### Discussion of Biases
Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.
For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.
### Other Known Limitations
If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.
## Additional Information
### Dataset Curators
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
MIT
| gusevski/factrueval2016 | [
"arxiv:2005.00614",
"region:us"
] | 2022-04-29T05:41:12+00:00 | {} | 2022-04-29T19:34:48+00:00 |
30d1fddbdb897932513e5974736b46c6fe6b6ceb | # AutoTrain Dataset for project: procell-expert
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project procell-expert.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "We studied the antitumor activity and toxicity of ZD1694 (tomudex), a specific inhibitor of thymidyl[...]",
"target": 0
},
{
"text": "Here we provide data that human prostate cancer cell lines express the platelet-type isoform of 12-L[...]",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['accept', 'reject'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 155 |
| valid | 40 |
| Mim/autotrain-data-procell-expert | [
"task_categories:text-classification",
"region:us"
] | 2022-04-29T07:23:02+00:00 | {"task_categories": ["text-classification"]} | 2022-04-29T07:27:03+00:00 |
474fa5e109d3675a21add2fc4cc0f201159545ec |
# WikiHow Entries
Contains wikiHow question, answer and summary in `.json` format. | soyasis/wikihow_small | [
"language:en",
"license:mit",
"region:us"
] | 2022-04-29T09:17:35+00:00 | {"language": "en", "license": "mit"} | 2022-04-29T09:24:34+00:00 |
0653eeaccbe5f0a9738220e0b1615f791da248b1 |
# Dataset Card for WMT21 Metrics Task
## 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:** [WMT21 Metrics Shared Task](https://www.statmt.org/wmt21/metrics-task.html)
- **Repository:** [MT Metrics Eval Github Repository](https://github.com/google-research/mt-metrics-eval)
- **Paper:** [Paper](https://aclanthology.org/2021.wmt-1.73/)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset comprises twenty language pairs:
- Bengali-Hindi (`bn-hi`)
- Czech-English (`cs-en`)
- German-English (`de-en`)
- German-French (`de-fr`)
- English-Czech (`en-cs`)
- English-German (`en-de`)
- English-Hausa (`en-ha`)
- English-Icelandic (`en-is`)
- English-Japanese (`en-ja`)
- English-Russian (`en-ru`)
- English-Chinese (`en-zh`)
- French-German (`fr-de`)
- Hausa-English (`ha-en`)
- Hindi-Bengali (`hi-bn`)
- Icelandic-English (`is-en`)
- Japenese-English (`ja-en`)
- Russian-English (`ru-en`)
- Xhosa-Zulu (`xh-zu`)
- Chinese-English (`zh-en`)
- Zulu-Xhosa (`zu-xh`)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
| muibk/wmt21_metrics_task | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:100K<n<1M",
"license:unknown",
"region:us"
] | 2022-04-29T09:52:47+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found", "machine-generated", "expert-generated"], "language": ["bn-hi", "cs-en", "de-en", "de-fr", "en-cs", "en-de", "en-ha", "en-is", "en-ja", "en-ru", "en-zh", "fr-de", "ha-en", "hi-bn", "is-en", "ja-en", "ru-en", "xh-zh", "zh-en", "zu-xh"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["100K<n<1M"], "source_datasets": [], "task_categories": ["translation"], "task_ids": [], "pretty_name": "WMT21 Metrics Shared Task"} | 2022-07-12T12:13:25+00:00 |
5f2f2089d47d9d4ba9d20d7cd792703d85b554bc | rish16/cs4243-database-dict | [
"license:mit",
"region:us"
] | 2022-04-29T10:56:42+00:00 | {"license": "mit"} | 2022-04-29T11:25:57+00:00 |
|
3d793d9bfee3ee10313e3fa736c80d310c3a7209 |
# Dataset Card for Common Voice Corpus 9.0
## 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://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 20217 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 14973 validated hours in 93 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Toki Pona, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): 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]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_9_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## 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
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
| mozilla-foundation/common_voice_9_0 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | 2022-04-29T15:49:21+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": {"ab": ["10K<n<100K"], "ar": ["100K<n<1M"], "as": ["n<1K"], "az": ["n<1K"], "ba": ["100K<n<1M"], "bas": ["1K<n<10K"], "be": ["100K<n<1M"], "bg": ["1K<n<10K"], "bn": ["100K<n<1M"], "br": ["10K<n<100K"], "ca": ["1M<n<10M"], "ckb": ["10K<n<100K"], "cnh": ["1K<n<10K"], "cs": ["10K<n<100K"], "cv": ["10K<n<100K"], "cy": ["100K<n<1M"], "da": ["1K<n<10K"], "de": ["100K<n<1M"], "dv": ["10K<n<100K"], "el": ["10K<n<100K"], "en": ["1M<n<10M"], "eo": ["1M<n<10M"], "es": ["100K<n<1M"], "et": ["10K<n<100K"], "eu": ["100K<n<1M"], "fa": ["100K<n<1M"], "fi": ["10K<n<100K"], "fr": ["100K<n<1M"], "fy-NL": ["10K<n<100K"], "ga-IE": ["1K<n<10K"], "gl": ["10K<n<100K"], "gn": ["1K<n<10K"], "ha": ["1K<n<10K"], "hi": ["10K<n<100K"], "hsb": ["1K<n<10K"], "hu": ["10K<n<100K"], "hy-AM": ["1K<n<10K"], "ia": ["10K<n<100K"], "id": ["10K<n<100K"], "ig": ["1K<n<10K"], "it": ["100K<n<1M"], "ja": ["10K<n<100K"], "ka": ["1K<n<10K"], "kab": ["100K<n<1M"], "kk": ["1K<n<10K"], "kmr": ["10K<n<100K"], "ky": ["10K<n<100K"], "lg": ["100K<n<1M"], "lt": ["10K<n<100K"], "lv": ["1K<n<10K"], "mdf": ["n<1K"], "mhr": ["10K<n<100K"], "mk": ["n<1K"], "ml": ["1K<n<10K"], "mn": ["10K<n<100K"], "mr": ["10K<n<100K"], "mt": ["10K<n<100K"], "myv": ["1K<n<10K"], "nan-tw": ["1K<n<10K"], "nl": ["10K<n<100K"], "nn-NO": ["n<1K"], "or": ["1K<n<10K"], "pa-IN": ["1K<n<10K"], "pl": ["100K<n<1M"], "pt": ["100K<n<1M"], "rm-sursilv": ["1K<n<10K"], "rm-vallader": ["1K<n<10K"], "ro": ["10K<n<100K"], "ru": ["100K<n<1M"], "rw": ["1M<n<10M"], "sah": ["1K<n<10K"], "sat": ["n<1K"], "sk": ["10K<n<100K"], "sl": ["10K<n<100K"], "sr": ["1K<n<10K"], "sv-SE": ["10K<n<100K"], "sw": ["100K<n<1M"], "ta": ["100K<n<1M"], "th": ["100K<n<1M"], "tig": ["n<1K"], "tok": ["1K<n<10K"], "tr": ["10K<n<100K"], "tt": ["10K<n<100K"], "ug": ["10K<n<100K"], "uk": ["10K<n<100K"], "ur": ["10K<n<100K"], "uz": ["100K<n<1M"], "vi": ["10K<n<100K"], "vot": ["n<1K"], "yue": ["10K<n<100K"], "zh-CN": ["10K<n<100K"], "zh-HK": ["100K<n<1M"], "zh-TW": ["100K<n<1M"]}, "source_datasets": ["extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "paperswithcode_id": "common-voice", "pretty_name": "Common Voice Corpus 9.0", "language_bcp47": ["ab", "ar", "as", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy-NL", "ga-IE", "gl", "gn", "ha", "hi", "hsb", "hu", "hy-AM", "ia", "id", "ig", "it", "ja", "ka", "kab", "kk", "kmr", "ky", "lg", "lt", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mt", "myv", "nan-tw", "nl", "nn-NO", "or", "pa-IN", "pl", "pt", "rm-sursilv", "rm-vallader", "ro", "ru", "rw", "sah", "sat", "sk", "sl", "sr", "sv-SE", "sw", "ta", "th", "tig", "tok", "tr", "tt", "ug", "uk", "ur", "uz", "vi", "vot", "yue", "zh-CN", "zh-HK", "zh-TW"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."} | 2023-07-29T15:00:12+00:00 |
8a8252c8c8e8c9a8f7ecaeb27bae1ac8b2313ab0 | Dataset containing city, country, region, and continents alongside their longitude and latitude co-ordinates. Cartesian coordinates are provided in x, y, z features. | jamescalam/world-cities-geo | [
"region:us"
] | 2022-04-29T15:54:48+00:00 | {} | 2022-04-29T17:34:46+00:00 |
03f4a9bf2ec961792476aee76a9fa8722ff8dc1e |
# VQGAN Pairs
This dataset contains ~2.4 million image pairs intended for improvement of image quality in VQGAN predictions. Each pair consists of:
- A 512x512 crop of an image taken from Open Images.
- A 256x256 image encoded and decoded using VQGAN, corresponding to the same image crop as the original.
This is the VQGAN implementation that was used for encoding and decoding: https://github.com/patil-suraj/vqgan-jax
# License
This dataset is created using Open Images, which has the following license:
The annotations are licensed by Google LLC under CC BY 4.0 license. The images are listed as having a CC BY 2.0 license. Note: while we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself. | dalle-mini/vqgan-pairs | [
"task_categories:other",
"source_datasets:Open Images",
"license:cc-by-4.0",
"license:cc-by-2.0",
"license:unknown",
"super-resolution",
"image-enhancement",
"region:us"
] | 2022-04-29T18:08:33+00:00 | {"license": ["cc-by-4.0", "cc-by-2.0", "unknown"], "source_datasets": ["Open Images"], "task_categories": ["other"], "task_ids": [], "pretty_name": "VQGAN Pairs", "tags": ["super-resolution", "image-enhancement"]} | 2022-10-25T12:56:57+00:00 |
3b03eda1bd275edf2d9caaccf07d32d2c237c0d2 |
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Enzyme Commission labels.
The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
The EC-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without EC tags are removed.
| DanielHesslow/SwissProt-EC | [
"Protein",
"Enzyme Commission",
"EC",
"region:us"
] | 2022-04-29T18:46:46+00:00 | {"language": ["protein sequences"], "datasets": ["Swissprot"], "tags": ["Protein", "Enzyme Commission", "EC"]} | 2022-04-30T14:12:33+00:00 |
0db4e0ea3153c4caa5c49e7387f65b78a8996148 |
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with PFam labels.
The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
The Pfam-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without Pfam tags are removed.
| DanielHesslow/SwissProt-Pfam | [
"Protein",
"PFam",
"region:us"
] | 2022-04-29T18:52:56+00:00 | {"language": ["protein sequences"], "datasets": ["Swissprot"], "tags": ["Protein", "PFam"]} | 2022-04-30T14:15:55+00:00 |
f64128a2e9e7a2756daacee8cc00e9b86142e19e |
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Gene Ontology labels.
The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
The GO-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without GO tags are removed.
| DanielHesslow/SwissProt-GO | [
"Protein",
"Gene Ontology",
"GO",
"region:us"
] | 2022-04-29T18:53:34+00:00 | {"language": ["protein sequences"], "datasets": ["Swissprot"], "tags": ["Protein", "Gene Ontology", "GO"]} | 2022-04-30T14:16:48+00:00 |
edc48764e7faeea87dbc8b157ddae26d3fb62408 | samhellkill/spacekitty-v1 | [
"license:other",
"region:us"
] | 2022-04-30T05:33:09+00:00 | {"license": "other"} | 2022-04-30T05:33:09+00:00 |
|
d59c4caa6cd95db6dea4a389b3195404aeaf5d5d | charly/test | [
"license:apache-2.0",
"region:us"
] | 2022-04-30T09:50:07+00:00 | {"license": "apache-2.0"} | 2022-04-30T16:17:22+00:00 |
|
74c9c46cc27003593171ef289c364f42d0f4286b |
# Dataset
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Enzyme Commission labels.
The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
The leaf level EC-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without leaf-level-EC tags are removed.
## Example
The protein Q87BZ2 have the following EC tags.
EC:2.-.-.- (Transferases)
EC:2.7.-.- (Transferring phosphorus-containing groups)
EC:2.7.1.- (Phosphotransferases with an alcohol group as acceptor)
EC:2.7.1.30 (Glycerol kinase)
We only extract the leaf level labels, here EC:2.7.1.30, corresponding to glycerol kinase.
| lightonai/SwissProt-EC-leaf | [
"Protein",
"Enzyme Commission",
"region:us"
] | 2022-04-30T14:14:51+00:00 | {"language": ["protein sequences"], "datasets": ["Swissprot"], "tags": ["Protein", "Enzyme Commission"]} | 2022-05-05T08:54:42+00:00 |
dc89ed1cc59ea92c19958c81c58070a2e95b02ab |
# Ukrainian Wikipedia Edits
### Dataset summary
A collection of over 5M sentence edits extracted from Ukrainian Wikipedia history revisions.
Edits were filtered by edit distance and sentence length. This makes them usable for grammatical error correction (GEC) or spellchecker models pre-training.
### Supported Tasks and Leaderboards
* Ukrainian grammatical error correction (GEC) - see [UA-GEC](https://github.com/grammarly/ua-gec)
* Ukrainian spelling correction
### Languages
Ukrainian
## Dataset Structure
### Data Fields
* `src` - sentence before edit
* `tgt` - sentence after edit
### Data Splits
* `full/train` contains all the data (5,243,376 samples)
* `tiny/train` contains a 5000 examples sample.
## Dataset Creation
Latest full Ukrainian Wiki dump were used as of 2022-04-30.
It was processed with the [wikiedits](https://github.com/snukky/wikiedits) and custom scripts.
### Source Data
#### Initial Data Collection and Normalization
Wikipedia
#### Who are the source language producers?
Wikipedia writers
### Annotations
#### Annotation process
Annotations inferred by comparing two subsequent page revisions.
#### Who are the annotators?
People who edit Wikipedia pages.
### Personal and Sensitive Information
No
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The data is noisy. In addition to GEC and spelling edits, it contains a good chunk of factual changes and vandalism.
More task-specific filters could help.
## Additional Information
### Dataset Curators
[Oleksiy Syvokon](https://github.com/asivokon)
### Licensing Information
CC-BY-3.0
### Citation Information
```
@inproceedings{wiked2014,
author = {Roman Grundkiewicz and Marcin Junczys-Dowmunt},
title = {The WikEd Error Corpus: A Corpus of Corrective Wikipedia Edits and its Application to Grammatical Error Correction},
booktitle = {Advances in Natural Language Processing -- Lecture Notes in Computer Science},
editor = {Adam Przepiórkowski and Maciej Ogrodniczuk},
publisher = {Springer},
year = {2014},
volume = {8686},
pages = {478--490},
url = {http://emjotde.github.io/publications/pdf/mjd.poltal2014.draft.pdf}
}
```
### Contributions
[@snukky](https://github.com/snukky) created tools for dataset processing.
[@asivokon](https://github.com/asivokon) generated this dataset.
| osyvokon/wiki-edits-uk | [
"task_categories:other",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"license:cc-by-3.0",
"region:us"
] | 2022-04-30T17:21:55+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["uk-UA"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual", "translation"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "pretty_name": "Ukrainian Wikipedia edits "} | 2022-07-02T18:06:25+00:00 |
c517b46f5b0574f716b9e3b173deb85d4db74236 | # AutoTrain Dataset for project: company
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project company.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"tokens": [
"sahil",
"prasad",
"president",
"www",
"swimcentre",
"com",
"banik",
"baalkrishan",
"gandhi",
"com",
"no",
"satish",
"nagar",
"hisar"
],
"tags": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
]
},
{
"tokens": [
"olivia",
"wilson",
"real",
"estate",
"agent",
"reallygreatsite",
"com",
"anywhere",
"st",
"any",
"city",
"st",
"www",
"reallygreatsite",
"com"
],
"tags": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"tags": "Sequence(feature=ClassLabel(num_classes=2, names=['0', '9'], id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 999651 |
| valid | 499630 |
| defector/autotrain-data-company | [
"language:en",
"region:us"
] | 2022-04-30T18:34:55+00:00 | {"language": ["en"]} | 2022-10-25T09:12:59+00:00 |
e8ae5ab634fd26487d0686488fd8abfb833ff283 | # Dataset Card for OSDG-CD
## 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:** [OSDG-CD homepage](https://zenodo.org/record/8397907)
### Dataset Summary
The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs).
> NOTES
>
> * There are currently no examples for SDGs 16 and 17. See [this GitHub issue](https://github.com/osdg-ai/osdg-data/issues/3).
> * As of July 2023, there areexamples also for SDG 16.
### Supported Tasks and Leaderboards
TBD
### Languages
The language of the dataset is English.
## Dataset Structure
### Data Instances
For each instance, there is a string for the text, a string for the SDG, and an integer for the label.
```
{'text': 'Each section states the economic principle, reviews international good practice and discusses the situation in Brazil.',
'label': 5}
```
The average token count for the premises and hypotheses are given below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Premise | 14.1 |
| Hypothesis | 8.3 |
### Data Fields
- `doi`: Digital Object Identifier of the original document
- `text_id`: unique text identifier
- `text`: text excerpt from the document
- `sdg`: the SDG the text is validated against
- `label`: an integer from `0` to `17` which corresponds to the `sdg` field
- `labels_negative`: the number of volunteers who rejected the suggested SDG label
- `labels_positive`: the number of volunteers who accepted the suggested SDG label
- `agreement`: agreement score based on the formula
### Data Splits
The OSDG-CD dataset has 1 splits: _train_.
| Dataset Split | Number of Instances in Split |
| ------------- |----------------------------- |
| Train | 32,327 |
## Dataset Creation
### Curation Rationale
The [The OSDG Community Dataset (OSDG-CD)](https://zenodo.org/record/8397907) was developed as a benchmark for ...
with the goal of producing a dataset large enough to train models using neural methodologies.
### Source Data
#### Initial Data Collection and Normalization
TBD
#### Who are the source language producers?
TBD
### Annotations
#### Annotation process
TBD
#### Who are the annotators?
TBD
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers.
## Considerations for Using the Data
### Social Impact of Dataset
TBD
## Additional Information
TBD
### Dataset Curators
TBD
### Licensing Information
The OSDG Community Dataset (OSDG-CD) is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@dataset{osdg_2023_8397907,
author = {OSDG and
UNDP IICPSD SDG AI Lab and
PPMI},
title = {OSDG Community Dataset (OSDG-CD)},
month = oct,
year = 2023,
note = {{This CSV file uses UTF-8 character encoding. For
easy access on MS Excel, open the file using Data
→ From Text/CSV. Please split CSV data into
different columns by using a TAB delimiter.}},
publisher = {Zenodo},
version = {2023.10},
doi = {10.5281/zenodo.8397907},
url = {https://doi.org/10.5281/zenodo.8397907}
}
```
### Contributions
TBD
| Filippo/osdg_cd | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-04-30T20:54:04+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "OSDG Community Dataset (OSDG-CD)", "dataset_info": {"config_name": "main_config", "features": [{"name": "doi", "dtype": "string"}, {"name": "text_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "sdg", "dtype": "uint16"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "SDG 1", "1": "SDG 2", "2": "SDG 3", "3": "SDG 4", "4": "SDG 5", "5": "SDG 6", "6": "SDG 7", "7": "SDG 8", "8": "SDG 9", "9": "SDG 10", "10": "SDG 11", "11": "SDG 12", "12": "SDG 13", "13": "SDG 14", "14": "SDG 15", "15": "SDG 16"}}}}, {"name": "labels_negative", "dtype": "uint16"}, {"name": "labels_positive", "dtype": "uint16"}, {"name": "agreement", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 30151244, "num_examples": 42355}], "download_size": 29770590, "dataset_size": 30151244}} | 2023-10-08T08:57:13+00:00 |
4ed58453467ac729dd815696584b8fad8dac4646 |
# RAFT submissions for my-raft-submission
## Submitting to the leaderboard
To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps:
1. Generate predictions on the unlabeled test set of each task
2. Validate the predictions are compatible with the evaluation framework
3. Push the predictions to the Hub!
See the instructions below for more details.
### Rules
1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week.
2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
### Submission file format
For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
* ID (int)
* Label (string)
See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
```python
from pathlib import Path
import pandas as pd
from collections import Counter
from datasets import load_dataset, get_dataset_config_names
tasks = get_dataset_config_names("ought/raft")
for task in tasks:
# Load dataset
raft_subset = load_dataset("ought/raft", task)
# Compute majority class over training set
counter = Counter(raft_subset["train"]["Label"])
majority_class = counter.most_common(1)[0][0]
# Load predictions file
preds = pd.read_csv(f"data/{task}/predictions.csv")
# Convert label IDs to label names
preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
# Save predictions
preds.to_csv(f"data/{task}/predictions.csv", index=False)
```
As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
```
data
├── ade_corpus_v2
│ ├── predictions.csv
│ └── task.json
├── banking_77
│ ├── predictions.csv
│ └── task.json
├── neurips_impact_statement_risks
│ ├── predictions.csv
│ └── task.json
├── one_stop_english
│ ├── predictions.csv
│ └── task.json
├── overruling
│ ├── predictions.csv
│ └── task.json
├── semiconductor_org_types
│ ├── predictions.csv
│ └── task.json
├── systematic_review_inclusion
│ ├── predictions.csv
│ └── task.json
├── tai_safety_research
│ ├── predictions.csv
│ └── task.json
├── terms_of_service
│ ├── predictions.csv
│ └── task.json
├── tweet_eval_hate
│ ├── predictions.csv
│ └── task.json
└── twitter_complaints
├── predictions.csv
└── task.json
```
### Validate your submission
To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
```
python cli.py validate
```
If everything is correct, you should see the following message:
```
All submission files validated! ✨ 🚀 ✨
Now you can make a submission 🤗
```
### Push your submission to the Hugging Face Hub!
The final step is to commit your files and push them to the Hub:
```
python cli.py submit
```
If there are no errors, you should see the following message:
```
Submission successful! 🎉 🥳 🎉
Your submission will be evaulated on Sunday 05 September 2021 ⏳
```
where the evaluation is run every Sunday and your results will be visible on the leaderboard. | Anon126/my-raft-submission | [
"benchmark:raft",
"region:us"
] | 2022-05-01T09:48:53+00:00 | {"benchmark": "raft", "type": "prediction", "submission_name": "none"} | 2022-05-01T09:50:18+00:00 |
869f3ba009c4258ae2d272e664931404de6ec67d | Rodion/uno_sustainable_development_goals | [
"license:afl-3.0",
"region:us"
] | 2022-05-01T09:59:28+00:00 | {"license": "afl-3.0"} | 2022-05-01T12:54:31+00:00 |
|
c3d2d2159db2fb34ca8ef05cbf96f21addeeea8b | annotations_creators:
- automatic
language_creators:
- found
languages:
- es-AR
licenses:
- cc0-1.0
multilinguality:
- monolingual
paperswithcode_id:
pretty_name: wikiner
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
license: cc
---
# Dataset Card for wikiner
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [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 Informatio]
- **Paper:** [Learning multilingual named entity recognition from Wikipedia](https://doi.org/10.1016/j.artint.2012.03.006)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [NazaGara](ngaragiola430@mi.unc.edu.ar)
### Dataset Summary
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example: [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
### Supported Tasks and Leaderboards
Named Entity Recognition (NER) is a subtask of Information Extraction. Different NER systems were evaluated as a part of the Sixth Message Understanding Conference in 1995 (MUC6). The target language was English. The participating systems performed well. However, many of them used language-specific resources for performing the task and it is unknown how they would have performed on another language than English.
After 1995 NER systems have been developed for some European languages and a few Asian languages. There have been at least two studies that have applied one NER system to different languages. Palmer and Day [PD97] have used statistical methods for finding named entities in newswire articles in Chinese, English, French, Japanese, Portuguese and Spanish. They found that the difficulty of the NER task was different for the six languages but that a large part of the task could be performed with simple methods. Cucerzan and Yarowsky [CY99] used both morphological and contextual clues for identifying named entities in English, Greek, Hindi, Rumanian and Turkish. With minimal supervision, they obtained overall F measures between 40 and 70, depending on the languages used.
- `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.
This dataset was used in order to train a Spanish NER model using [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).
### Languages
The only supported language is spanish (es).
## Dataset Structure
### Data Fields
The dictionary to map the id to the Label names is:
{
0: 'O',
1: 'B-PER',
2: 'I-PER',
3: 'B-ORG',
4: 'I-ORG',
5: 'B-LOC',
6: 'I-LOC',
7: 'B-MISC',
8: 'I-MISC'
}
### Data Splits
The only split is the train split.
Number of examples = 128355
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
Created by Nothman et al. at 2013.
### 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] | NazaGara/wikiner-es | [
"region:us"
] | 2022-05-01T12:57:29+00:00 | {} | 2022-08-14T14:01:57+00:00 |
082b1de183522ccd43858713564b51b36ee28f93 | hongdijk/kor_nlu_hufsice2 | [
"license:other",
"region:us"
] | 2022-05-01T13:12:05+00:00 | {"license": "other"} | 2022-05-23T15:33:29+00:00 |
|
5cbdab6346ed40a167a4dca0bbd91ef7eeda736d | hongdijk/kor_nlu_hufs | [
"license:cc-by-sa-4.0",
"region:us"
] | 2022-05-01T14:02:11+00:00 | {"license": "cc-by-sa-4.0"} | 2022-05-20T09:25:34+00:00 |
|
fed332421fbf9afc326f2490ec5bb49ffec180f3 |
# Dataset Card for ToxiGen
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-instances)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
## Sign up for Data Access
To access ToxiGen, first fill out [this form](https://forms.office.com/r/r6VXX8f8vh).
## Dataset Description
- **Repository:** https://github.com/microsoft/toxigen
- **Paper:** https://arxiv.org/abs/2203.09509
- **Point of Contact #1:** [Tom Hartvigsen](tomh@mit.edu)
- **Point of Contact #2:** [Saadia Gabriel](skgabrie@cs.washington.edu)
### Dataset Summary
This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in [our paper](https://arxiv.org/abs/2203.09509).
### Languages
All text is written in English.
## Dataset Structure
### Data Fields
We release TOXIGEN as a dataframe with the following fields:
- **prompt** is the prompt used for **generation**.
- **generation** is the TOXIGEN generated text.
- **generation_method** denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used.
- **prompt_label** is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign).
- **group** indicates the target group of the prompt.
- **roberta_prediction** is the probability predicted by our corresponding RoBERTa model for each instance.
### Citation Information
```bibtex
@inproceedings{hartvigsen2022toxigen,
title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection},
author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022}
}
```
| skg/toxigen-data | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"arxiv:2203.09509",
"region:us"
] | 2022-05-01T14:49:02+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["machine-generated"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["hate-speech-detection"], "pretty_name": "ToxiGen", "languages": ["en-US"], "licenses": []} | 2022-06-20T10:12:11+00:00 |
c44ea85c786b78a24aeefc0fcbe5c5c66648f720 | Diegomejia/ds1ucb | [
"license:mit",
"region:us"
] | 2022-05-02T01:03:54+00:00 | {"license": "mit"} | 2022-05-02T01:03:54+00:00 |
|
b52b6486b38d74ddaf95626b15e0f0c50fa5e959 |
# Dataset Card for WIT
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [WIT homepage](https://github.com/google-research-datasets/wit)
- **Repository:** [WIT repository](https://github.com/google-research-datasets/wit)
- **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
](https://arxiv.org/abs/2103.01913)
- **Leaderboard:** [WIT leaderboard](https://www.kaggle.com/c/wikipedia-image-caption)
- **Point of Contact:** [WIT e-mail](mailto:wit-dataset@google.com)
### Dataset Summary
Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
A few unique advantages of WIT:
* The largest multimodal dataset (time of this writing) by the number of image-text examples.
* A massively multilingual (first of its kind) with coverage for over 100+ languages.
* A collection of diverse set of concepts and real world entities.
* Brings forth challenging real-world test sets.
### 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
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": get_datasets_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("wit")
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 a model for image captioning where the goal is to predict a caption given the image.
- `text-retrieval`: The goal in this task is to build a model that retrieves the text closest to an image.
In these tasks, any combination of the `caption_reference_description`, `caption_attribution_description` and `caption_alt_text_description` fields can be used as the input text/caption.
### Languages
The dataset contains examples from all Wikipedia languages, with the following stats:
Image-Text | # Lang | Uniq. Images | # Lang
------------ | ------ | ------------- | ------
total > 1M | 9 | images > 1M | 6
total > 500K | 10 | images > 500K | 12
total > 100K | 36 | images > 100K | 35
total > 50K | 15 | images > 50K | 17
total > 14K | 38 | images > 13K | 38
## Dataset Structure
### Data Instances
```
{
'language': 'en',
'page_url': 'https://en.wikipedia.org/wiki/Oxydactylus',
'image_url': 'https://upload.wikimedia.org/wikipedia/commons/5/5f/Oxydactylus_longipes_fm.jpg',
'page_title': 'Oxydactylus',
'section_title': None,
'hierarchical_section_title': 'Oxydactylus',
'caption_reference_description': None,
'caption_attribution_description': 'English: Mounted skeleton of Oxydactylus longipes in the Field Museum of Natural History.',
'caption_alt_text_description': None,
'mime_type': 'image/jpeg',
'original_height': 3564,
'original_width': 2748,
'is_main_image': True,
'attribution_passes_lang_id': True,
'page_changed_recently': True,
'context_page_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene, existing for approximately 14 million years. The name is from the Ancient Greek οξύς and δάκτυλος.\nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.',
'context_section_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene (28.4–13.7 mya), existing for approximately 14 million years. The name is from the Ancient Greek οξύς (oxys, "sharp")and δάκτυλος (daktylos, "finger").\n \nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.'
}
```
### Data Fields
- `language`: Language code depicting wikipedia language of the page
- `page_url`: URL to wikipedia page
- `image_url`: URL to wikipedia image
- `page_title`: Wikipedia page's title
- `section_title`: Section's title
- `hierarchical_section_title`: Hierarchical section's title
- `caption_reference_description`: This is the caption that is visible on the wiki page directly below the image.
- `caption_attribution_description`: This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias and thus can be in a language different to the original page article.
- `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers
- `mime_type`: Mime type associated to the image.
- `original_height`: Image height
- `original_width`: Image width
- `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.
- `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description).
- `page_changed_recently`: [More Information Needed]
- `context_page_description`: Page description corresponds to the short description of the page. It provides a concise explanation of the scope of the page.
- `context_section_description`: Text within the image's section.
<p align='center'>
<img width='75%' src='https://production-media.paperswithcode.com/datasets/Screenshot_2021-03-04_at_14.26.02.png' alt="Half Dome" /> </br>
<b>Figure: WIT annotation example. </b>
</p>
Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913)
### Data Splits
All data is held in `train` split, with a total of 37046386 rows.
## Dataset Creation
### Curation Rationale
From the [repository](https://github.com/google-research-datasets/wit#motivation):
> Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding.
>
> To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets.
>
> The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs).
### Source Data
#### Initial Data Collection and Normalization
From the [paper, section 3.1](https://arxiv.org/abs/2103.01913):
> We started with all Wikipedia content pages (i.e., ignoring other
pages that have discussions, comments and such). These number about ∼124M pages across 279 languages.
#### Who are the source language producers?
Text was extracted from Wikipedia.
### Annotations
#### Annotation process
WIT was constructed using an automatic process. However it was human-validated.
From the [paper, section 3.7](https://arxiv.org/abs/2103.01913):
> To further verify the quality of the WIT dataset we performed a
study using (crowd-sourced) human annotators. As seen in Fig. 3,
we asked raters to answer 3 questions. Given an image and the page
title, raters first evaluate the quality of the attribution description
and reference description in the first two questions (order randomized). The third question understands the contextual quality of these
text descriptions given the page description and caption. Each response is on a 3-point scale: "Yes" if the text perfectly describes
the image, "Maybe" if it is sufficiently explanatory and "No" if it is
irrelevant or the image is inappropriate.
#### 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
From the [paper, section 3.4](https://arxiv.org/abs/2103.01913):
> Lastly we found that certain image-text pairs occurred very
frequently. These were often generic images that did not have
much to do with the main article page. Common examples
included flags, logos, maps, insignia and such. To prevent
biasing the data, we heavily under-sampled all such images
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
}
```
### Contributions
Thanks to [@thomasw21](https://github.com/thomasw21), [@nateraw](https://github.com/nateraw) and [hassiahk](https://github.com/hassiahk) for adding this dataset. | google/wit | [
"task_categories:text-retrieval",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"source_datasets:extended|wikipedia",
"language:af",
"language:ar",
"language:ast",
"language:azb",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:cs",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:hr",
"language:hu",
"language:hy",
"language:id",
"language:it",
"language:iw",
"language:ja",
"language:ka",
"language:ko",
"language:la",
"language:lt",
"language:lv",
"language:mk",
"language:ml",
"language:ms",
"language:nl",
"language:nn",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sk",
"language:sl",
"language:sr",
"language:sv",
"language:th",
"language:tr",
"language:uk",
"language:ur",
"language:vi",
"language:vo",
"language:zh",
"license:cc-by-sa-3.0",
"arxiv:2103.01913",
"region:us"
] | 2022-05-02T10:22:32+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["af", "ar", "ast", "azb", "be", "bg", "bn", "br", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "hr", "hu", "hy", "id", "it", "iw", "ja", "ka", "ko", "la", "lt", "lv", "mk", "ml", "ms", "nl", "nn", "no", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "th", "tr", "uk", "ur", "vi", "vo", "zh"], "license": ["cc-by-sa-3.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original", "extended|wikipedia"], "task_categories": ["text-retrieval", "image-to-text"], "task_ids": ["text-retrieval-other-text-image-retrieval", "image-captioning"], "paperswithcode_id": "wit", "pretty_name": "Wikipedia-based Image Text"} | 2022-07-04T09:47:07+00:00 |
4f517807d7ecf96e4686674ead3a445b22f4b9b1 | Davincilee/door_inner_with_SAE | [
"license:other",
"region:us"
] | 2022-05-02T13:13:11+00:00 | {"license": "other"} | 2022-05-02T13:16:14+00:00 |
|
82895de1f6c69a73fd9c4fdb2d6adff0a8fbc4b4 |
## Dataset Description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI email](mailto:idx_aqua@sil.org)
- **Source Data:** [Bloom Library](https://bloomlibrary.org/)
 
## Dataset Summary
**Bloom** is free, open-source software and an associated website [Bloom Library](https://bloomlibrary.org/), app, and services developed by [SIL International](https://www.sil.org/). Bloom’s primary goal is to equip non-dominant language communities and their members to create the literature they want for their community and children. Bloom also serves organizations that help such communities develop literature and education or other aspects of community development.
This version of the Bloom Library data is developed specifically for the image captioning task. It includes data from 351 languages across 31 language families. There is a mean of 32 stories and 319 image-caption pairs per language.
**Note**: If you speak one of these languages and can help provide feedback or corrections, please let us know!
**Note**: Although this data was used in the training of the [BLOOM model](https://huggingface.co/bigscience/bloom), this dataset only represents a small portion of the data used to train that model. Data from "Bloom Library" was combined with a large number of other datasets to train that model. "Bloom Library" is a project that existed prior to the BLOOM model, and is something separate. All that to say... We were using the "Bloom" name before it was cool. 😉
## Languages
Of the 500+ languages listed at BloomLibrary.org, there are 351 languages available in this dataset. Here are the corresponding ISO 639-3 codes:
aaa, abc, ada, adq, aeu, afr, agq, ags, ahk, aia, ajz, aka, ame, amh, amp, amu, ann, aph, awa, awb, azn, azo, bag, bam, baw, bax, bbk, bcc, bce, bec, bef, ben, bfd, bfm, bfn, bgf, bho, bhs, bis, bjn, bjr, bkc, bkh, bkm, bkx, bob, bod, boz, bqm, bra, brb, bri, brv, bss, bud, buo, bwt, bwx, bxa, bya, bze, bzi, cak, cbr, ceb, cgc, chd, chp, cim, clo, cmn, cmo, csw, cuh, cuv, dag, ddg, ded, deu, dig, dje, dmg, dnw, dtp, dtr, dty, dug, eee, ekm, enb, enc, eng, ewo, fas, fil, fli, fon, fra, fub, fuh, gal, gbj, gou, gsw, guc, guj, guz, gwc, hao, hat, hau, hbb, hig, hil, hin, hla, hna, hre, hro, idt, ilo, ind, ino, isu, ita, jgo, jmx, jpn, jra, kak, kam, kan, kau, kbq, kbx, kby, kek, ken, khb, khm, kik, kin, kir, kjb, kmg, kmr, kms, kmu, kor, kqr, krr, ksw, kur, kvt, kwd, kwu, kwx, kxp, kyq, laj, lan, lao, lbr, lfa, lgg, lgr, lhm, lhu, lkb, llg, lmp, lns, loh, lsi, lts, lug, luy, lwl, mai, mal, mam, mar, mdr, mfh, mfj, mgg, mgm, mgo, mgq, mhx, miy, mkz, mle, mlk, mlw, mmu, mne, mnf, mnw, mot, mqj, mrn, mry, msb, muv, mve, mxu, mya, myk, myx, mzm, nas, nco, nep, new, nge, ngn, nhx, njy, nla, nld, nlv, nod, nsk, nsn, nso, nst, nuj, nwe, nwi, nxa, nxl, nya, nyo, nyu, nza, odk, oji, oki, omw, ori, ozm, pae, pag, pan, pbt, pce, pcg, pdu, pea, pex, pis, pkb, pmf, pnz, por, psp, pwg, qub, quc, quf, quz, qve, qvh, qvm, qvo, qxh, rel, rnl, ron, roo, rue, rug, rus, san, saq, sat, sdk, sea, sgd, shn, sml, snk, snl, som, sot, sox, spa, sps, ssn, stk, swa, swh, sxb, syw, taj, tam, tbj, tdb, tdg, tdt, teo, tet, tgk, tha, the, thk, thl, thy, tio, tkd, tnl, tnn, tnp, tnt, tod, tom, tpi, tpl, tpu, tsb, tsn, tso, tuv, tuz, tvs, udg, unr, urd, uzb, ven, vie, vif, war, wbm, wbr, wms, wni, wnk, wtk, xho, xkg, xmd, xmg, xmm, xog, xty, yas, yav, ybb, ybh, ybi, ydd, yea, yet, yid, yin, ymp, zaw, zho, zlm, zuh, zul
## Dataset Statistics
Some of the languages included in the dataset just include 1 or a couple of "stories." These are not split between training, validation, and test. For those with higher numbers of available stories we include the following statistics:
| ISO 639-3 | stories | image-caption pairs |
|:------------|-----------:|-----------------------:|
| ahk | 101 | 907 |
| awa | 163 | 1200 |
| bam | 4 | 86 |
| ben | 251 | 2235 |
| bho | 173 | 1172 |
| boz | 5 | 102 |
| bzi | 66 | 497 |
| cak | 67 | 817 |
| ceb | 418 | 2953 |
| cgc | 197 | 1638 |
| chd | 1 | 84 |
| dty | 172 | 1310 |
| eng | 2633 | 28618 |
| fas | 129 | 631 |
| fra | 403 | 5278 |
| hat | 260 | 2411 |
| hau | 256 | 1865 |
| hbb | 27 | 273 |
| ind | 259 | 2177 |
| jra | 139 | 1423 |
| kak | 195 | 1416 |
| kan | 21 | 168 |
| kek | 36 | 621 |
| kir | 382 | 4026 |
| kjb | 102 | 984 |
| kor | 132 | 2773 |
| mai | 180 | 1211 |
| mai | 180 | 1211 |
| mam | 134 | 1317 |
| mhx | 98 | 945 |
| mya | 38 | 421 |
| myk | 34 | 341 |
| nep | 200 | 1507 |
| new | 177 | 1225 |
| por | 163 | 3101 |
| quc | 99 | 817 |
| rus | 353 | 3933 |
| sdk | 11 | 153 |
| snk | 35 | 356 |
| spa | 528 | 6111 |
| stk | 7 | 113 |
| tgl | 0 | 0 |
| tha | 285 | 3023 |
| thl | 185 | 1464 |
| tpi | 201 | 2162 |
## Dataset Structure
### Data Instances
The examples look like this for Hausa:
```
from datasets import load_dataset
# Specify the language code.
dataset = load_dataset("sil-ai/bloom-captioning", iso639_3_letter_code,
use_auth_token=True, download_mode='force_redownload')
# An entry in the dataset consists of a image caption along with
# a link to the corresponding image (and various pieces of metadata).
print(dataset['train'][0])
```
This would produce an output:
```
{'image_id': '5e7e2ab6-493f-4430-a635-695fbff76cf0',
'image_url': 'https://bloom-vist.s3.amazonaws.com/%E0%A4%AF%E0%A5%87%E0%A4%B8%E0%A5%81%20%E0%A4%9A%E0%A5%81%E0%A4%B5%E0%A4%BE%20%E0%A4%89%E0%A4%A0%E0%A5%81%E0%A4%99%E0%A5%8D%E2%80%8C%E0%A4%99%E0%A4%BF%20%E0%A4%B2%E0%A4%BE%E0%A4%AE%E0%A5%8D%E2%80%8C%E0%A4%9F%E0%A4%BF%E0%A4%AF%E0%A4%BE%E0%A4%A8%E0%A4%BE/image2.jpg',
'caption': 'Lokacinan almajiran suna tuƙa jirgin ruwansu, amma can cikin dare sun kai tsakiyar tafkin kaɗai. Suna tuƙi da wahala saboda iska tana busawa da ƙarfi gaba da su.',
'story_id': 'cd17125d-66c6-467c-b6c3-7463929faff9',
'album_id': 'a3074fc4-b88f-4769-a6de-dc952fdb35f0',
'original_bloom_language_tag': 'ha',
'index_in_story': 0}
```
To download all of the images locally directory `images`, you can do something similar to the following:
```
from PIL import Image
import urllib
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):
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
if 'png' in image_url:
png = Image.open(io.BytesIO(req.read())).convert('RGBA')
png.load() # required for png.split()
background = Image.new("RGB", png.size, (255, 255, 255))
background.paste(png, mask=png.split()[3]) # 3 is the alpha channel
image_id = str(uuid.uuid4())
image_path = "images/" + image_id + ".jpg"
background.save(image_path, 'JPEG', quality=80)
else:
image = Image.open(io.BytesIO(req.read()))
image_id = str(uuid.uuid4())
image_path = "images/" + image_id + ".jpg"
image.save(image_path)
return image_path
def fetch_images(batch, num_threads, timeout=None, retries=3):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image_path"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dataset = dataset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Data Fields
The metadata fields below are available:
- **image_id**: a unique ID for the image
- **image_url**: a link for downloading the image
- **caption**: a caption corresponding to the image
- **story_id**: a unique ID for the corresponding story in which the caption appears
- **album_id**: a unique ID for the corresponding album in which the image appears
- **original_bloom_language_tag**: the original language identification from the Bloom library
- **index_in_story**: an index corresponding to the order of the image-caption pair in the corresponding story
### Data Splits
All languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.
**NOTE:** The captions for the test split are currently hidden due to on ongoing shared task competition. They have been replaced by a placeholder `<hidden>` token.
## Changelog
- **25 October 2022** - Initial release
- **25 October 2022** - Update to include licenses on each data item.
| sil-ai/bloom-captioning | [
"task_ids:image-captioning",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:afr",
"language:af",
"language:aaa",
"language:abc",
"language:ada",
"language:adq",
"language:aeu",
"language:agq",
"language:ags",
"language:ahk",
"language:aia",
"language:ajz",
"language:aka",
"language:ak",
"language:ame",
"language:amh",
"language:am",
"language:amp",
"language:amu",
"language:ann",
"language:aph",
"language:awa",
"language:awb",
"language:azn",
"language:azo",
"language:bag",
"language:bam",
"language:bm",
"language:baw",
"language:bax",
"language:bbk",
"language:bcc",
"language:bce",
"language:bec",
"language:bef",
"language:ben",
"language:bn",
"language:bfd",
"language:bfm",
"language:bfn",
"language:bgf",
"language:bho",
"language:bhs",
"language:bis",
"language:bi",
"language:bjn",
"language:bjr",
"language:bkc",
"language:bkh",
"language:bkm",
"language:bkx",
"language:bob",
"language:bod",
"language:bo",
"language:boz",
"language:bqm",
"language:bra",
"language:brb",
"language:bri",
"language:brv",
"language:bss",
"language:bud",
"language:buo",
"language:bwt",
"language:bwx",
"language:bxa",
"language:bya",
"language:bze",
"language:bzi",
"language:cak",
"language:cbr",
"language:ceb",
"language:cgc",
"language:chd",
"language:chp",
"language:cim",
"language:clo",
"language:cmn",
"language:zh",
"language:cmo",
"language:csw",
"language:cuh",
"language:cuv",
"language:dag",
"language:ddg",
"language:ded",
"language:deu",
"language:de",
"language:dig",
"language:dje",
"language:dmg",
"language:dnw",
"language:dtp",
"language:dtr",
"language:dty",
"language:dug",
"language:eee",
"language:ekm",
"language:enb",
"language:enc",
"language:eng",
"language:en",
"language:ewo",
"language:fas",
"language:fa",
"language:fil",
"language:fli",
"language:fon",
"language:fra",
"language:fr",
"language:fub",
"language:fuh",
"language:gal",
"language:gbj",
"language:gou",
"language:gsw",
"language:guc",
"language:guj",
"language:gu",
"language:guz",
"language:gwc",
"language:hao",
"language:hat",
"language:ht",
"language:hau",
"language:ha",
"language:hbb",
"language:hig",
"language:hil",
"language:hin",
"language:hi",
"language:hla",
"language:hna",
"language:hre",
"language:hro",
"language:idt",
"language:ilo",
"language:ind",
"language:id",
"language:ino",
"language:isu",
"language:ita",
"language:it",
"language:jgo",
"language:jmx",
"language:jpn",
"language:ja",
"language:jra",
"language:kak",
"language:kam",
"language:kan",
"language:kn",
"language:kau",
"language:kr",
"language:kbq",
"language:kbx",
"language:kby",
"language:kek",
"language:ken",
"language:khb",
"language:khm",
"language:km",
"language:kik",
"language:ki",
"language:kin",
"language:rw",
"language:kir",
"language:ky",
"language:kjb",
"language:kmg",
"language:kmr",
"language:ku",
"language:kms",
"language:kmu",
"language:kor",
"language:ko",
"language:kqr",
"language:krr",
"language:ksw",
"language:kur",
"language:kvt",
"language:kwd",
"language:kwu",
"language:kwx",
"language:kxp",
"language:kyq",
"language:laj",
"language:lan",
"language:lao",
"language:lo",
"language:lbr",
"language:lfa",
"language:lgg",
"language:lgr",
"language:lhm",
"language:lhu",
"language:lkb",
"language:llg",
"language:lmp",
"language:lns",
"language:loh",
"language:lsi",
"language:lts",
"language:lug",
"language:lg",
"language:luy",
"language:lwl",
"language:mai",
"language:mal",
"language:ml",
"language:mam",
"language:mar",
"language:mr",
"language:mdr",
"language:mfh",
"language:mfj",
"language:mgg",
"language:mgm",
"language:mgo",
"language:mgq",
"language:mhx",
"language:miy",
"language:mkz",
"language:mle",
"language:mlk",
"language:mlw",
"language:mmu",
"language:mne",
"language:mnf",
"language:mnw",
"language:mot",
"language:mqj",
"language:mrn",
"language:mry",
"language:msb",
"language:muv",
"language:mve",
"language:mxu",
"language:mya",
"language:my",
"language:myk",
"language:myx",
"language:mzm",
"language:nas",
"language:nco",
"language:nep",
"language:ne",
"language:new",
"language:nge",
"language:ngn",
"language:nhx",
"language:njy",
"language:nla",
"language:nld",
"language:nl",
"language:nlv",
"language:nod",
"language:nsk",
"language:nsn",
"language:nso",
"language:nst",
"language:nuj",
"language:nwe",
"language:nwi",
"language:nxa",
"language:nxl",
"language:nya",
"language:ny",
"language:nyo",
"language:nyu",
"language:nza",
"language:odk",
"language:oji",
"language:oj",
"language:oki",
"language:omw",
"language:ori",
"language:or",
"language:ozm",
"language:pae",
"language:pag",
"language:pan",
"language:pa",
"language:pbt",
"language:pce",
"language:pcg",
"language:pdu",
"language:pea",
"language:pex",
"language:pis",
"language:pkb",
"language:pmf",
"language:pnz",
"language:por",
"language:pt",
"language:psp",
"language:pwg",
"language:qaa",
"language:qub",
"language:quc",
"language:quf",
"language:quz",
"language:qve",
"language:qvh",
"language:qvm",
"language:qvo",
"language:qxh",
"language:rel",
"language:rnl",
"language:ron",
"language:ro",
"language:roo",
"language:rue",
"language:rug",
"language:rus",
"language:ru",
"language:san",
"language:sa",
"language:saq",
"language:sat",
"language:sdk",
"language:sea",
"language:sgd",
"language:shn",
"language:sml",
"language:snk",
"language:snl",
"language:som",
"language:so",
"language:sot",
"language:st",
"language:sox",
"language:spa",
"language:es",
"language:sps",
"language:ssn",
"language:stk",
"language:swa",
"language:sw",
"language:swh",
"language:sxb",
"language:syw",
"language:taj",
"language:tam",
"language:ta",
"language:tbj",
"language:tdb",
"language:tdg",
"language:tdt",
"language:teo",
"language:tet",
"language:tgk",
"language:tg",
"language:tha",
"language:th",
"language:the",
"language:thk",
"language:thl",
"language:thy",
"language:tio",
"language:tkd",
"language:tnl",
"language:tnn",
"language:tnp",
"language:tnt",
"language:tod",
"language:tom",
"language:tpi",
"language:tpl",
"language:tpu",
"language:tsb",
"language:tsn",
"language:tn",
"language:tso",
"language:ts",
"language:tuv",
"language:tuz",
"language:tvs",
"language:udg",
"language:unr",
"language:urd",
"language:ur",
"language:uzb",
"language:uz",
"language:ven",
"language:ve",
"language:vie",
"language:vi",
"language:vif",
"language:war",
"language:wbm",
"language:wbr",
"language:wms",
"language:wni",
"language:wnk",
"language:wtk",
"language:xho",
"language:xh",
"language:xkg",
"language:xmd",
"language:xmg",
"language:xmm",
"language:xog",
"language:xty",
"language:yas",
"language:yav",
"language:ybb",
"language:ybh",
"language:ybi",
"language:ydd",
"language:yea",
"language:yet",
"language:yid",
"language:yi",
"language:yin",
"language:ymp",
"language:zaw",
"language:zho",
"language:zlm",
"language:zuh",
"language:zul",
"language:zu",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-05-02T13:24:03+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["afr", "af", "aaa", "abc", "ada", "adq", "aeu", "agq", "ags", "ahk", "aia", "ajz", "aka", "ak", "ame", "amh", "am", "amp", "amu", "ann", "aph", "awa", "awb", "azn", "azo", "bag", "bam", "bm", "baw", "bax", "bbk", "bcc", "bce", "bec", "bef", "ben", "bn", "bfd", "bfm", "bfn", "bgf", "bho", "bhs", "bis", "bi", "bjn", "bjr", "bkc", "bkh", "bkm", "bkx", "bob", "bod", "bo", "boz", "bqm", "bra", "brb", "bri", "brv", "bss", "bud", "buo", "bwt", "bwx", "bxa", "bya", "bze", "bzi", "cak", "cbr", "ceb", "cgc", "chd", "chp", "cim", "clo", "cmn", "zh", "cmo", "csw", "cuh", "cuv", "dag", "ddg", "ded", "deu", "de", "dig", "dje", "dmg", "dnw", "dtp", "dtr", "dty", "dug", "eee", "ekm", "enb", "enc", "eng", "en", "ewo", "fas", "fa", "fil", "fli", "fon", "fra", "fr", "fub", "fuh", "gal", "gbj", "gou", "gsw", "guc", "guj", "gu", "guz", "gwc", "hao", "hat", "ht", "hau", "ha", "hbb", "hig", "hil", "hin", "hi", "hla", "hna", "hre", "hro", "idt", "ilo", "ind", "id", "ino", "isu", "ita", "it", "jgo", "jmx", "jpn", "ja", "jra", "kak", "kam", "kan", "kn", "kau", "kr", "kbq", "kbx", "kby", "kek", "ken", "khb", "khm", "km", "kik", "ki", "kin", "rw", "kir", "ky", "kjb", "kmg", "kmr", "ku", "kms", "kmu", "kor", "ko", "kqr", "krr", "ksw", "kur", "ku", "kvt", "kwd", "kwu", "kwx", "kxp", "kyq", "laj", "lan", "lao", "lo", "lbr", "lfa", "lgg", "lgr", "lhm", "lhu", "lkb", "llg", "lmp", "lns", "loh", "lsi", "lts", "lug", "lg", "luy", "lwl", "mai", "mal", "ml", "mam", "mar", "mr", "mdr", "mfh", "mfj", "mgg", "mgm", "mgo", "mgq", "mhx", "miy", "mkz", "mle", "mlk", "mlw", "mmu", "mne", "mnf", "mnw", "mot", "mqj", "mrn", "mry", "msb", "muv", "mve", "mxu", "mya", "my", "myk", "myx", "mzm", "nas", "nco", "nep", "ne", "new", "nge", "ngn", "nhx", "njy", "nla", "nld", "nl", "nlv", "nod", "nsk", "nsn", "nso", "nst", "nuj", "nwe", "nwi", "nxa", "nxl", "nya", "ny", "nyo", "nyu", "nza", "odk", "oji", "oj", "oki", "omw", "ori", "or", "ozm", "pae", "pag", "pan", "pa", "pbt", "pce", "pcg", "pdu", "pea", "pex", "pis", "pkb", "pmf", "pnz", "por", "pt", "psp", "pwg", "qaa", "qub", "quc", "quf", "quz", "qve", "qvh", "qvm", "qvo", "qxh", "rel", "rnl", "ron", "ro", "roo", "rue", "rug", "rus", "ru", "san", "sa", "saq", "sat", "sdk", "sea", "sgd", "shn", "sml", "snk", "snl", "som", "so", "sot", "st", "sox", "spa", "es", "sps", "ssn", "stk", "swa", "sw", "swh", "sxb", "syw", "taj", "tam", "ta", "tbj", "tdb", "tdg", "tdt", "teo", "tet", "tgk", "tg", "tha", "th", "the", "thk", "thl", "thy", "tio", "tkd", "tnl", "tnn", "tnp", "tnt", "tod", "tom", "tpi", "tpl", "tpu", "tsb", "tsn", "tn", "tso", "ts", "tuv", "tuz", "tvs", "udg", "unr", "urd", "ur", "uzb", "uz", "ven", "ve", "vie", "vi", "vif", "war", "wbm", "wbr", "wms", "wni", "wnk", "wtk", "xho", "xh", "xkg", "xmd", "xmg", "xmm", "xog", "xty", "yas", "yav", "ybb", "ybh", "ybi", "ydd", "yea", "yet", "yid", "yi", "yin", "ymp", "zaw", "zho", "zh", "zlm", "zuh", "zul", "zu"], "license": ["cc-by-nc-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_ids": ["image-captioning"], "pretty_name": "BloomCaptioning", "extra_gated_prompt": "One more step before getting this dataset. This dataset is open access and available only for non-commercial use (except for portions of the dataset labeled explicitly with a `cc-by-sa` license). A \"license\" field paired with each of the dataset entries/samples specifies the Creative Commons license for that entry/sample.\n\nThese [Creative Commons licenses](https://creativecommons.org/about/cclicenses/) specify that: \n\n1. You cannot use the dataset for or directed toward commercial advantage or monetary compensation (except for those portions of the dataset labeled specifically with a `cc-by-sa` license. If you would like to ask about commercial uses of this dataset, please [email us](mailto:sj@derivation.co).\n2. Any public, non-commercial use of the data must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. \n3. For those portions of the dataset marked with an ND license, you cannot remix, transform, or build upon the material, and you may not distribute modified material. \n\nIn addition to the above implied by Creative Commons and when clicking \"Access Repository\" below, you agree: \n\n1. Not to use the dataset for any use intended to or which has the effect of harming or enabling discrimination against individuals or groups based on legally protected characteristics or categories, including but not limited to discrimination against Indigenous People as outlined in Articles 2; 13-16; and 31 of the United Nations Declaration on the Rights of Indigenous People, 13 September 2007 and as subsequently amended and revised.\n2. That your *contact information* (email address and username) can be shared with the model authors as well.\n ", "extra_gated_fields": {"I have read the License and agree with its terms": "checkbox"}} | 2022-12-10T02:16:13+00:00 |
d9a3cfd6830ce040b34c1169d564227de87d5bf8 |
# Dataset Card for "crd3"
## 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:** [CRD3 homepage](https://github.com/RevanthRameshkumar/CRD3)
- **Repository:** [CRD3 repository](https://github.com/RevanthRameshkumar/CRD3)
- **Paper:** [Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset](https://www.aclweb.org/anthology/2020.acl-main.459/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 279.93 MB
- **Size of the generated dataset:** 4020.33 MB
- **Total amount of disk used:** 4300.25 MB
### Dataset Summary
Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.
Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.
The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding
abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player
collaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,
and semantic ties to the previous dialogues.
### Supported Tasks and Leaderboards
`summarization`: The dataset can be used to train a model for abstractive summarization. A [fast abstractive summarization-RL](https://github.com/ChenRocks/fast_abs_rl) model was presented as a baseline, which achieves ROUGE-L-F1 of 25.18.
### Languages
The text in the dataset is in English, as spoken by actors on The Critical Role show, which is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game.
## Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
#### default
- **Size of downloaded dataset files:** 279.93 MB
- **Size of the generated dataset:** 4020.33 MB
- **Total amount of disk used:** 4300.25 MB
An example of 'train' looks as follows.
```
{
"alignment_score": 3.679936647415161,
"chunk": "Wish them a Happy Birthday on their Facebook and Twitter pages! Also, as a reminder: D&D Beyond streams their weekly show (\"And Beyond\") every Wednesday on twitch.tv/dndbeyond.",
"chunk_id": 1,
"turn_end": 6,
"turn_num": 4,
"turn_start": 4,
"turns": {
"names": ["SAM"],
"utterances": ["Yesterday, guys, was D&D Beyond's first one--", "first one-year anniversary. Take two. Hey guys,", "yesterday was D&D Beyond's one-year anniversary.", "Wish them a happy birthday on their Facebook and", "Twitter pages."]
}
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `chunk`: a `string` feature.
- `chunk_id`: a `int32` feature.
- `turn_start`: a `int32` feature.
- `turn_end`: a `int32` feature.
- `alignment_score`: a `float32` feature.
- `turn_num`: a `int32` feature.
- `turns`: a dictionary feature containing:
- `names`: a `string` feature.
- `utterances`: a `string` feature.
### Data Splits
| name | train |validation| test |
|-------|------:|---------:|------:|
|default|26,232| 3,470|4,541|
## Dataset Creation
### Curation Rationale
Dialogue understanding and abstractive summarization remain both important and challenging problems for computational linguistics. Current paradigms in summarization modeling have specific failures in capturing semantics and pragmatics, content selection, rewriting, and evaluation in the domain of long, story-telling dialogue. CRD3 offers a linguistically rich dataset to explore these domains.
### Source Data
#### Initial Data Collection and Normalization
Dungeons and Dragons is a popular roleplaying game that is driven by structured storytelling. Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons. This dataset consists of 159 episodes of the show, where the episodes are transcribed. Inconsistencies (e.g. spelling of speaker names) were manually resolved.
The abstractive summaries were collected from the [Critical Role Fandom wiki](https://criticalrole.fandom.com/)
#### Who are the source language producers?
The language producers are actors on The Critical Role show, which is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
CRTranscript provided transcripts of the show; contributors of the Critical Role Wiki provided the abstractive summaries.
### Licensing Information
This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa-4.0]., as corresponding to the Critical Role Wiki https://criticalrole.fandom.com/
### Citation Information
```
@inproceedings{
title = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},
author = {Rameshkumar, Revanth and Bailey, Peter},
year = {2020},
publisher = {Association for Computational Linguistics},
conference = {ACL}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.
| shanya/crd3 | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-05-02T14:01:40+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization", "text-generation", "fill-mask"], "task_ids": ["dialogue-modeling"], "paperswithcode_id": "crd3", "pretty_name": "CRD3 (Critical Role Dungeons and Dragons Dataset)"} | 2022-10-25T09:13:08+00:00 |
ff6d4fb32fd566d3a1fa20e946cba3234179465e |
# Dataset Card for WIT
## 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:** [WIT homepage](https://github.com/google-research-datasets/wit)
- **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
](https://arxiv.org/abs/2103.01913)
- **Leaderboard:** [WIT leaderboard](https://paperswithcode.com/sota/text-image-retrieval-on-wit) and [WIT Kaggle competition](https://www.kaggle.com/competitions/wikipedia-image-caption/leaderboard)
- **Point of Contact:** [Miriam Redi](mailto:miriam@wikimedia.org)
### Dataset Summary
Wikimedia's version of the Wikipedia-based Image Text (WIT) Dataset, a large multimodal multilingual dataset.
From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/):
> The core training data is taken from the Wikipedia Image-Text (WIT) Dataset, a large curated set of more than 37 million image-text associations extracted from Wikipedia articles in 108 languages that was recently released by Google Research.
>
> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images. However, due to licensing and data volume issues, the Google dataset only provides the image name and corresponding URL for download and not the raw image files.
>
> Getting easy access to the image files is crucial for participants to successfully develop competitive models. Therefore, today, the Wikimedia Research team is releasing its first large image dataset. It contains more than six million image files from Wikipedia articles in 100+ languages, which correspond to almost [1] all captioned images in the WIT dataset. Image files are provided at a 300-px resolution, a size that is suitable for most of the learning frameworks used to classify and analyze images.
> [1] We are publishing all images having a non-null “reference description” in the WIT dataset. For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the RetinaFace detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are candidate for deletion on Commons from the dataset.
**Note**: Compared to [Google's version](https://huggingface.co/datasets/google/wit), which has contents of one Wikipedia page per data sample, this version groups contents of all Wikipedia pages available in different languages for the image in one single data sample to avoid duplication of image bytes.
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.
- `text-retrieval`: The goal in this task is to build a model that retrieves the text (`caption_title_and_reference_description`) closest to an image. The leaderboard for this task can be found [here](https://paperswithcode.com/sota/text-image-retrieval-on-wit). This task also has a competition on [Kaggle](https://www.kaggle.com/c/wikipedia-image-caption).
In these tasks, any combination of the `caption_reference_description`, `caption_attribution_description` and `caption_alt_text_description` fields can be used as the input text/caption.
### Languages
The dataset contains examples from all Wikipedia languages.
## Dataset Structure
### Data Instances
Each instance is an image, its representation in bytes, a pre-computed embedding, and the set of captions attached to the image in Wikipedia.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x225 at 0x7F88F3876358>,
'image_url': 'https://upload.wikimedia.org/wikipedia/commons/8/8b/Scolopendra_gigantea.jpg',
'embedding': [1.4784087, 2.8710432, 0.0, 0.51603067, ..., 10.266883, 0.51142216, 0.0, 2.3464653],
'metadata_url': 'http://commons.wikimedia.org/wiki/File:Scolopendra_gigantea.jpg',
'original_height': 3000,
'original_width': 4000,
'mime_type': 'image/jpeg',
'caption_attribution_description': 'English: Puerto Rican Giant Centipede, Scolopendra gigantea; Vieques, Puerto Rico Slovenčina: Stonožka obrovská, Scolopendra gigantea; Vieques, Portoriko',
'wit_features': {
'language': ['ro', 'vi', 'sk', ..., 'nl', 'th', 'lv'],
'page_url': ['https://ro.wikipedia.org/wiki/Scolopendra_gigantea', 'https://vi.wikipedia.org/wiki/Scolopendra_gigantea', 'https://sk.wikipedia.org/wiki/Scolopendra_gigantea', ..., 'https://nl.wikipedia.org/wiki/Scolopendra_gigantea', 'https://th.wikipedia.org/wiki/%E0%B8%95%E0%B8%B0%E0%B8%82%E0%B8%B2%E0%B8%9A%E0%B8%A2%E0%B8%B1%E0%B8%81%E0%B8%A9%E0%B9%8C%E0%B8%82%E0%B8%B2%E0%B9%80%E0%B8%AB%E0%B8%A5%E0%B8%B7%E0%B8%AD%E0%B8%87%E0%B9%80%E0%B8%9B%E0%B8%A3%E0%B8%B9', 'https://lv.wikipedia.org/wiki/Skolopendru_dzimta'],
'attribution_passes_lang_id': [True, True, True, ..., True, True, True],
'caption_alt_text_description': [None, None, None, ..., 'Scolopendra gigantea', None, 'Milzu skolopendra (Scolopendra gigantea)'],
'caption_reference_description': [None, None, None, ..., None, None, 'Milzu skolopendra (Scolopendra gigantea)'],
'caption_title_and_reference_description': [None, 'Scolopendra gigantea [SEP] ', None, ..., 'Scolopendra gigantea [SEP] ', None, 'Skolopendru dzimta [SEP] Milzu skolopendra (Scolopendra gigantea)'],
'context_page_description': ['Scolopendra gigantea este un miriapod din clasa Chilopoda, fiind cel mai mare reprezentant al genului Scolopendra. Adultul poate atinge o lungime de 26 cm, uneori depășind 30 cm. Această specie habitează în regiunile de nord și de vest a Americii de Sud, pe insulele Trinidad, insulele Virgine, Jamaica Hispaniola ș.a. Localnicii denumesc scolopendra chilopodul gigant galben și chilopodul gigant amazonian.', 'Scolopendra gigantea là đại diện lớn nhất của chi Scolopendra nói riêng và cả lớp rết nói chung, thường đạt độ dài 26 cm và có thể vượt quá 30 cm. Sinh sống ở khu vực phía bắc và tây của Nam Mỹ và các đảo Trinidad, Puerto Rico, Saint Thomas, U.S. Virgin Islands, Jamaica, và Hispaniola.', 'Scolopendra gigantea, starší slovenský nazov: štípavica veľká, je živočích z rodu Scolopendra, s veľkosťou do 30 cm.', ..., 'Scolopendra gigantea is een tijgerduizendpoot uit Zuid-Amerika. De soort jaagt onder andere op grote geleedpotigen, amfibieën, reptielen en kleine zoogdieren. Het is voor zover bekend de grootste niet uitgestorven duizendpoot ter wereld.', 'ตะขาบยักษ์ขาเหลืองเปรู หรือ ตะขาบยักษ์อเมซอน เป็นตะขาบชนิดที่มีขนาดใหญ่ที่สุดในสกุล Scolopendra โดยปกติเมื่อโตเต็มที่จะยาว 26 เซนติเมตร แต่บางครั้งก็สามารถโตได้ถึง 30 เซนติเมตร ตะขาบชนิดนี้อาศัยอยู่ทางแถบเหนือและตะวันตกของทวีปอเมริกาใต้ และตามเกาะแก่งของประเทศตรินิแดดและจาไมกา เป็นสัตว์กินเนื้อ โดยกินจิ้งจก, กบ, นก, หนู และแม้แต่ค้างคาวเป็นอาหาร และขึ้นชื่อในเรื่องความดุร้าย', 'Skolpendru dzimta pieder pie simtkāju kārtas. Ap 400 dzimtas sugas sastopamas visā pasaulē, īpaši subtropu un tropu apgabalos. Mitinās augsnē, nobirušās lapās, plaisās, spraugās.'],
'context_section_description': [None, 'Scolopendra gigantea (còn được gọi là Rết chân vàng khổng lồ Peru và Rết khổng lồ Amazon) là đại diện lớn nhất của chi Scolopendra nói riêng và cả lớp rết nói chung, thường đạt độ dài 26\xa0cm (10\xa0in) và có thể vượt quá 30\xa0cm (12\xa0in). Sinh sống ở khu vực phía bắc và tây của Nam Mỹ và các đảo Trinidad, Puerto Rico, Saint Thomas, U.S. Virgin Islands, Jamaica, và Hispaniola.', None, ..., 'Scolopendra gigantea is een tijgerduizendpoot uit Zuid-Amerika. De soort jaagt onder andere op grote geleedpotigen, amfibieën, reptielen en kleine zoogdieren. Het is voor zover bekend de grootste niet uitgestorven duizendpoot ter wereld.', None, 'Skolpendru dzimta (Scolopendridae) pieder pie simtkāju kārtas. Ap 400 dzimtas sugas sastopamas visā pasaulē, īpaši subtropu un tropu apgabalos. Mitinās augsnē, nobirušās lapās, plaisās, spraugās.'],
'hierarchical_section_title': ['Scolopendra gigantea', 'Scolopendra gigantea', 'Scolopendra gigantea', ..., 'Scolopendra gigantea', 'ตะขาบยักษ์ขาเหลืองเปรู', 'Skolopendru dzimta'],
'is_main_image': [True, True, True, ..., True, True, True],
'page_title': ['Scolopendra gigantea', 'Scolopendra gigantea', 'Scolopendra gigantea', ..., 'Scolopendra gigantea', 'ตะขาบยักษ์ขาเหลืองเปรู', 'Skolopendru dzimta'],
'section_title': [None, None, None, ..., None, None, None]
}
}
```
**Note**: The dataset is stored in Parquet for better performance. This dataset was generated from the original files using [this script](wit_base/blob/main/scripts/wit.py). Additionally, 120 examples from the original files have incorrectly formatted one or more of the following fields: `original_height`, `original_width`, `mime_type` and `caption_attribution_description`. The fixed versions of these examples that were used in the generation script can be found [here](wit_base/blob/main/scripts/corrected_examples.py).
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image resized to a width of 300-px while preserving its aspect ratio. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `image_url`: URL to wikipedia image
- `embedding`: Precomputed image embedding. Each image is described with a 2048-dimensional signature extracted from the second-to-last layer of a [ResNet-50](https://arxiv.org/abs/1512.03385) neural network trained with [Imagenet](https://www.image-net.org/) data. These embeddings contain rich information about the image content and layout, in a compact form
- `metadata_url`: URL to wikimedia page containing the image and the metadata
- `original_height`: Original image height before resizing
- `original_width`: Original image width before resizing
- `mime_type`: Mime type associated to the image
- `caption_attribution_description`: This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias.
- `wit_features`: Sequence of captions for the image with language, page URL, information about the page, caption text, etc.
- `language`: Language code depicting wikipedia language of the page
- `page_url`: URL to wikipedia page
- `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description.
- `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers
- `caption_reference_description`: This is the caption that is visible on the wikipedia page directly below the image.
- `caption_title_and_reference_description`: Concatenation of `page_title` and `caption_reference_description`.
- `context_page_description`: Corresponds to the short description of the page. It provides a concise explanation of the scope of the page.
- `context_section_description`: Text within the image's section
- `hierarchical_section_title`: Hierarchical section's title
- `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.
- `page_changed_recently`: [More Information Needed]
- `page_title`: Wikipedia page's title
- `section_title`: Section's title
<p align='center'>
<img width='75%' src='https://production-media.paperswithcode.com/datasets/Screenshot_2021-03-04_at_14.26.02.png' alt="Half Dome" /> </br>
<b>Figure: WIT annotation example. </b>
</p>
Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913)
### Data Splits
All data is held in `train` split, with a total of 6477255 examples.
## Dataset Creation
### Curation Rationale
From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/):
> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images.
> Getting easy access to the image files is crucial for participants to successfully develop competitive models.
> With this large release of visual data, we aim to help the competition participants—as well as researchers and practitioners who are interested in working with Wikipedia images—find and download the large number of image files associated with the challenge, in a compact form.
### Source Data
#### Initial Data Collection and Normalization
From the [paper, section 3.1](https://arxiv.org/abs/2103.01913):
> We started with all Wikipedia content pages (i.e., ignoring other
pages that have discussions, comments and such). These number about ~124M pages across 279 languages.
#### Who are the source language producers?
Text was extracted from Wikipedia.
### Annotations
#### Annotation process
WIT was constructed using an automatic process. However it was human-validated.
From the [paper, section 3.7](https://arxiv.org/abs/2103.01913):
> To further verify the quality of the WIT dataset we performed a
study using (crowd-sourced) human annotators. As seen in Fig. 3,
we asked raters to answer 3 questions. Given an image and the page
title, raters first evaluate the quality of the attribution description
and reference description in the first two questions (order randomized). The third question understands the contextual quality of these
text descriptions given the page description and caption. Each response is on a 3-point scale: "Yes" if the text perfectly describes
the image, "Maybe" if it is sufficiently explanatory and "No" if it is
irrelevant or the image is inappropriate.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/#FN1):
> For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the [RetinaFace](https://arxiv.org/abs/1905.00641) detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are [candidate for deletion](https://commons.wikimedia.org/wiki/Commons:Deletion_requests) on Commons from the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
From the [paper, section 3.4](https://arxiv.org/abs/2103.01913):
> Lastly we found that certain image-text pairs occurred very
frequently. These were often generic images that did not have
much to do with the main article page. Common examples
included flags, logos, maps, insignia and such. To prevent
biasing the data, we heavily under-sampled all such images
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Miriam Redi, Fabian Kaelin and Tiziano Piccardi.
### Licensing Information
[CC BY-SA 4.0 international license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```bibtex
@article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
}
```
### Contributions
Thanks to [@nateraw](https://github.com/nateraw), [yjernite](https://github.com/yjernite) and [mariosasko](https://github.com/mariosasko) for adding this dataset. | wikimedia/wit_base | [
"task_categories:image-to-text",
"task_categories:text-retrieval",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|wikipedia",
"language:af",
"language:an",
"language:ar",
"language:arz",
"language:ast",
"language:az",
"language:azb",
"language:ba",
"language:bar",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:bs",
"language:ca",
"language:ce",
"language:ceb",
"language:ckb",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fil",
"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:hi",
"language:hr",
"language:hsb",
"language:ht",
"language:hu",
"language:hy",
"language:ia",
"language:id",
"language:io",
"language:is",
"language:it",
"language:iw",
"language:ja",
"language:jv",
"language:ka",
"language:kk",
"language:kn",
"language:ko",
"language:la",
"language:lah",
"language:lb",
"language:lmo",
"language:lt",
"language:lv",
"language:mg",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:ms",
"language:my",
"language:nan",
"language:nds",
"language:ne",
"language:nl",
"language:nn",
"language:no",
"language:nv",
"language:oc",
"language:pa",
"language:pl",
"language:pt",
"language:qu",
"language:ro",
"language:ru",
"language:sco",
"language:si",
"language:sk",
"language:sl",
"language:sq",
"language:sr",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:tg",
"language:th",
"language:tr",
"language:tt",
"language:uk",
"language:ur",
"language:uz",
"language:vec",
"language:vi",
"language:vo",
"language:war",
"language:xmf",
"language:yue",
"language:zh",
"license:cc-by-sa-4.0",
"text-image-retrieval",
"arxiv:2103.01913",
"arxiv:1512.03385",
"arxiv:1905.00641",
"region:us"
] | 2022-05-02T15:08:58+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["af", "an", "ar", "arz", "ast", "az", "azb", "ba", "bar", "be", "bg", "bn", "br", "bs", "ca", "ce", "ceb", "ckb", "cs", "cv", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gl", "hi", "hr", "hsb", "ht", "hu", "hy", "ia", "id", "io", "is", "it", "iw", "ja", "jv", "ka", "kk", "kn", "ko", "la", "lah", "lb", "lmo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "nan", "nds", "ne", "nl", "nn", "no", "nv", "oc", "pa", "pl", "pt", "qu", "ro", "ru", "sco", "si", "sk", "sl", "sq", "sr", "sv", "sw", "ta", "te", "tg", "th", "tr", "tt", "uk", "ur", "uz", "vec", "vi", "vo", "war", "xmf", "yue", "zh"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original", "extended|wikipedia"], "task_categories": ["image-to-text", "text-retrieval"], "task_ids": ["image-captioning"], "paperswithcode_id": "wit", "pretty_name": "Wikipedia-based Image Text", "language_bcp47": ["af", "an", "ar", "arz", "ast", "az", "azb", "ba", "bar", "be", "be-tarask", "bg", "bn", "br", "bs", "ca", "ce", "ceb", "ckb", "cs", "cv", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gl", "hi", "hr", "hsb", "ht", "hu", "hy", "ia", "id", "io", "is", "it", "iw", "ja", "jv", "ka", "kk", "kn", "ko", "la", "lah", "lb", "lmo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "nan", "nds", "ne", "nl", "nn", "no", "nv", "oc", "pa", "pl", "pt", "qu", "ro", "ru", "sco", "si", "sk", "sl", "sq", "sr", "sr-Latn", "sv", "sw", "ta", "te", "tg", "th", "tr", "tt", "uk", "ur", "uz", "vec", "vi", "vo", "war", "xmf", "yue", "zh", "zh-TW"], "tags": ["text-image-retrieval"]} | 2022-11-04T15:09:33+00:00 |
014711311cec8b5959350c373878a3311caeb764 |
# Dataset Card for ImageNet
## 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-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://image-net.org/index.php
- **Repository:**
- **Paper:** https://arxiv.org/abs/1409.0575
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171
- **Point of Contact:** mailto: imagenet.help.desk@gmail.com
### Dataset Summary
ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.
💡 This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used **subset** of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the [patch](https://drive.google.com/file/d/16RYnHpVOW0XKCsn3G3S9GTHUyoV2-4WX/view) which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](https://ieeexplore.ieee.org/abstract/document/5206848), please check the download section of the [main website](https://image-net.org/download-images.php).
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171).
To evaluate the `imagenet-classification` accuracy on the test split, one must first create an account at https://image-net.org. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at https://image-net.org/challenges/LSVRC/eval_server.php The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:
```
670 778 794 387 650
217 691 564 909 364
737 369 430 531 124
755 930 755 512 152
```
The export format is described in full in "readme.txt" within the 2013 development kit available here: https://image-net.org/data/ILSVRC/2013/ILSVRC2013_devkit.tgz. Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See `imagenet2012_labels.txt`.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
An example looks like below:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
'label': 23
}
```
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `label`: an `int` classification label. -1 for `test` set as the labels are missing.
The labels are indexed based on a sorted list of synset ids such as `n07565083` which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file [LOC_synset_mapping.txt](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt) available on Kaggle challenge page. You can also use `dataset_instance.features["labels"].int2str` function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.
<details>
<summary>
Click here to see the full list of ImageNet class labels mapping:
</summary>
|id|Class|
|--|-----|
|0 | tench, Tinca tinca|
|1 | goldfish, Carassius auratus|
|2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias|
|3 | tiger shark, Galeocerdo cuvieri|
|4 | hammerhead, hammerhead shark|
|5 | electric ray, crampfish, numbfish, torpedo|
|6 | stingray|
|7 | cock|
|8 | hen|
|9 | ostrich, Struthio camelus|
|10 | brambling, Fringilla montifringilla|
|11 | goldfinch, Carduelis carduelis|
|12 | house finch, linnet, Carpodacus mexicanus|
|13 | junco, snowbird|
|14 | indigo bunting, indigo finch, indigo bird, Passerina cyanea|
|15 | robin, American robin, Turdus migratorius|
|16 | bulbul|
|17 | jay|
|18 | magpie|
|19 | chickadee|
|20 | water ouzel, dipper|
|21 | kite|
|22 | bald eagle, American eagle, Haliaeetus leucocephalus|
|23 | vulture|
|24 | great grey owl, great gray owl, Strix nebulosa|
|25 | European fire salamander, Salamandra salamandra|
|26 | common newt, Triturus vulgaris|
|27 | eft|
|28 | spotted salamander, Ambystoma maculatum|
|29 | axolotl, mud puppy, Ambystoma mexicanum|
|30 | bullfrog, Rana catesbeiana|
|31 | tree frog, tree-frog|
|32 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui|
|33 | loggerhead, loggerhead turtle, Caretta caretta|
|34 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea|
|35 | mud turtle|
|36 | terrapin|
|37 | box turtle, box tortoise|
|38 | banded gecko|
|39 | common iguana, iguana, Iguana iguana|
|40 | American chameleon, anole, Anolis carolinensis|
|41 | whiptail, whiptail lizard|
|42 | agama|
|43 | frilled lizard, Chlamydosaurus kingi|
|44 | alligator lizard|
|45 | Gila monster, Heloderma suspectum|
|46 | green lizard, Lacerta viridis|
|47 | African chameleon, Chamaeleo chamaeleon|
|48 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis|
|49 | African crocodile, Nile crocodile, Crocodylus niloticus|
|50 | American alligator, Alligator mississipiensis|
|51 | triceratops|
|52 | thunder snake, worm snake, Carphophis amoenus|
|53 | ringneck snake, ring-necked snake, ring snake|
|54 | hognose snake, puff adder, sand viper|
|55 | green snake, grass snake|
|56 | king snake, kingsnake|
|57 | garter snake, grass snake|
|58 | water snake|
|59 | vine snake|
|60 | night snake, Hypsiglena torquata|
|61 | boa constrictor, Constrictor constrictor|
|62 | rock python, rock snake, Python sebae|
|63 | Indian cobra, Naja naja|
|64 | green mamba|
|65 | sea snake|
|66 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus|
|67 | diamondback, diamondback rattlesnake, Crotalus adamanteus|
|68 | sidewinder, horned rattlesnake, Crotalus cerastes|
|69 | trilobite|
|70 | harvestman, daddy longlegs, Phalangium opilio|
|71 | scorpion|
|72 | black and gold garden spider, Argiope aurantia|
|73 | barn spider, Araneus cavaticus|
|74 | garden spider, Aranea diademata|
|75 | black widow, Latrodectus mactans|
|76 | tarantula|
|77 | wolf spider, hunting spider|
|78 | tick|
|79 | centipede|
|80 | black grouse|
|81 | ptarmigan|
|82 | ruffed grouse, partridge, Bonasa umbellus|
|83 | prairie chicken, prairie grouse, prairie fowl|
|84 | peacock|
|85 | quail|
|86 | partridge|
|87 | African grey, African gray, Psittacus erithacus|
|88 | macaw|
|89 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita|
|90 | lorikeet|
|91 | coucal|
|92 | bee eater|
|93 | hornbill|
|94 | hummingbird|
|95 | jacamar|
|96 | toucan|
|97 | drake|
|98 | red-breasted merganser, Mergus serrator|
|99 | goose|
|100 | black swan, Cygnus atratus|
|101 | tusker|
|102 | echidna, spiny anteater, anteater|
|103 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus|
|104 | wallaby, brush kangaroo|
|105 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus|
|106 | wombat|
|107 | jellyfish|
|108 | sea anemone, anemone|
|109 | brain coral|
|110 | flatworm, platyhelminth|
|111 | nematode, nematode worm, roundworm|
|112 | conch|
|113 | snail|
|114 | slug|
|115 | sea slug, nudibranch|
|116 | chiton, coat-of-mail shell, sea cradle, polyplacophore|
|117 | chambered nautilus, pearly nautilus, nautilus|
|118 | Dungeness crab, Cancer magister|
|119 | rock crab, Cancer irroratus|
|120 | fiddler crab|
|121 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica|
|122 | American lobster, Northern lobster, Maine lobster, Homarus americanus|
|123 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish|
|124 | crayfish, crawfish, crawdad, crawdaddy|
|125 | hermit crab|
|126 | isopod|
|127 | white stork, Ciconia ciconia|
|128 | black stork, Ciconia nigra|
|129 | spoonbill|
|130 | flamingo|
|131 | little blue heron, Egretta caerulea|
|132 | American egret, great white heron, Egretta albus|
|133 | bittern|
|134 | crane|
|135 | limpkin, Aramus pictus|
|136 | European gallinule, Porphyrio porphyrio|
|137 | American coot, marsh hen, mud hen, water hen, Fulica americana|
|138 | bustard|
|139 | ruddy turnstone, Arenaria interpres|
|140 | red-backed sandpiper, dunlin, Erolia alpina|
|141 | redshank, Tringa totanus|
|142 | dowitcher|
|143 | oystercatcher, oyster catcher|
|144 | pelican|
|145 | king penguin, Aptenodytes patagonica|
|146 | albatross, mollymawk|
|147 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus|
|148 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca|
|149 | dugong, Dugong dugon|
|150 | sea lion|
|151 | Chihuahua|
|152 | Japanese spaniel|
|153 | Maltese dog, Maltese terrier, Maltese|
|154 | Pekinese, Pekingese, Peke|
|155 | Shih-Tzu|
|156 | Blenheim spaniel|
|157 | papillon|
|158 | toy terrier|
|159 | Rhodesian ridgeback|
|160 | Afghan hound, Afghan|
|161 | basset, basset hound|
|162 | beagle|
|163 | bloodhound, sleuthhound|
|164 | bluetick|
|165 | black-and-tan coonhound|
|166 | Walker hound, Walker foxhound|
|167 | English foxhound|
|168 | redbone|
|169 | borzoi, Russian wolfhound|
|170 | Irish wolfhound|
|171 | Italian greyhound|
|172 | whippet|
|173 | Ibizan hound, Ibizan Podenco|
|174 | Norwegian elkhound, elkhound|
|175 | otterhound, otter hound|
|176 | Saluki, gazelle hound|
|177 | Scottish deerhound, deerhound|
|178 | Weimaraner|
|179 | Staffordshire bullterrier, Staffordshire bull terrier|
|180 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier|
|181 | Bedlington terrier|
|182 | Border terrier|
|183 | Kerry blue terrier|
|184 | Irish terrier|
|185 | Norfolk terrier|
|186 | Norwich terrier|
|187 | Yorkshire terrier|
|188 | wire-haired fox terrier|
|189 | Lakeland terrier|
|190 | Sealyham terrier, Sealyham|
|191 | Airedale, Airedale terrier|
|192 | cairn, cairn terrier|
|193 | Australian terrier|
|194 | Dandie Dinmont, Dandie Dinmont terrier|
|195 | Boston bull, Boston terrier|
|196 | miniature schnauzer|
|197 | giant schnauzer|
|198 | standard schnauzer|
|199 | Scotch terrier, Scottish terrier, Scottie|
|200 | Tibetan terrier, chrysanthemum dog|
|201 | silky terrier, Sydney silky|
|202 | soft-coated wheaten terrier|
|203 | West Highland white terrier|
|204 | Lhasa, Lhasa apso|
|205 | flat-coated retriever|
|206 | curly-coated retriever|
|207 | golden retriever|
|208 | Labrador retriever|
|209 | Chesapeake Bay retriever|
|210 | German short-haired pointer|
|211 | vizsla, Hungarian pointer|
|212 | English setter|
|213 | Irish setter, red setter|
|214 | Gordon setter|
|215 | Brittany spaniel|
|216 | clumber, clumber spaniel|
|217 | English springer, English springer spaniel|
|218 | Welsh springer spaniel|
|219 | cocker spaniel, English cocker spaniel, cocker|
|220 | Sussex spaniel|
|221 | Irish water spaniel|
|222 | kuvasz|
|223 | schipperke|
|224 | groenendael|
|225 | malinois|
|226 | briard|
|227 | kelpie|
|228 | komondor|
|229 | Old English sheepdog, bobtail|
|230 | Shetland sheepdog, Shetland sheep dog, Shetland|
|231 | collie|
|232 | Border collie|
|233 | Bouvier des Flandres, Bouviers des Flandres|
|234 | Rottweiler|
|235 | German shepherd, German shepherd dog, German police dog, alsatian|
|236 | Doberman, Doberman pinscher|
|237 | miniature pinscher|
|238 | Greater Swiss Mountain dog|
|239 | Bernese mountain dog|
|240 | Appenzeller|
|241 | EntleBucher|
|242 | boxer|
|243 | bull mastiff|
|244 | Tibetan mastiff|
|245 | French bulldog|
|246 | Great Dane|
|247 | Saint Bernard, St Bernard|
|248 | Eskimo dog, husky|
|249 | malamute, malemute, Alaskan malamute|
|250 | Siberian husky|
|251 | dalmatian, coach dog, carriage dog|
|252 | affenpinscher, monkey pinscher, monkey dog|
|253 | basenji|
|254 | pug, pug-dog|
|255 | Leonberg|
|256 | Newfoundland, Newfoundland dog|
|257 | Great Pyrenees|
|258 | Samoyed, Samoyede|
|259 | Pomeranian|
|260 | chow, chow chow|
|261 | keeshond|
|262 | Brabancon griffon|
|263 | Pembroke, Pembroke Welsh corgi|
|264 | Cardigan, Cardigan Welsh corgi|
|265 | toy poodle|
|266 | miniature poodle|
|267 | standard poodle|
|268 | Mexican hairless|
|269 | timber wolf, grey wolf, gray wolf, Canis lupus|
|270 | white wolf, Arctic wolf, Canis lupus tundrarum|
|271 | red wolf, maned wolf, Canis rufus, Canis niger|
|272 | coyote, prairie wolf, brush wolf, Canis latrans|
|273 | dingo, warrigal, warragal, Canis dingo|
|274 | dhole, Cuon alpinus|
|275 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus|
|276 | hyena, hyaena|
|277 | red fox, Vulpes vulpes|
|278 | kit fox, Vulpes macrotis|
|279 | Arctic fox, white fox, Alopex lagopus|
|280 | grey fox, gray fox, Urocyon cinereoargenteus|
|281 | tabby, tabby cat|
|282 | tiger cat|
|283 | Persian cat|
|284 | Siamese cat, Siamese|
|285 | Egyptian cat|
|286 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor|
|287 | lynx, catamount|
|288 | leopard, Panthera pardus|
|289 | snow leopard, ounce, Panthera uncia|
|290 | jaguar, panther, Panthera onca, Felis onca|
|291 | lion, king of beasts, Panthera leo|
|292 | tiger, Panthera tigris|
|293 | cheetah, chetah, Acinonyx jubatus|
|294 | brown bear, bruin, Ursus arctos|
|295 | American black bear, black bear, Ursus americanus, Euarctos americanus|
|296 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus|
|297 | sloth bear, Melursus ursinus, Ursus ursinus|
|298 | mongoose|
|299 | meerkat, mierkat|
|300 | tiger beetle|
|301 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle|
|302 | ground beetle, carabid beetle|
|303 | long-horned beetle, longicorn, longicorn beetle|
|304 | leaf beetle, chrysomelid|
|305 | dung beetle|
|306 | rhinoceros beetle|
|307 | weevil|
|308 | fly|
|309 | bee|
|310 | ant, emmet, pismire|
|311 | grasshopper, hopper|
|312 | cricket|
|313 | walking stick, walkingstick, stick insect|
|314 | cockroach, roach|
|315 | mantis, mantid|
|316 | cicada, cicala|
|317 | leafhopper|
|318 | lacewing, lacewing fly|
|319 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk|
|320 | damselfly|
|321 | admiral|
|322 | ringlet, ringlet butterfly|
|323 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus|
|324 | cabbage butterfly|
|325 | sulphur butterfly, sulfur butterfly|
|326 | lycaenid, lycaenid butterfly|
|327 | starfish, sea star|
|328 | sea urchin|
|329 | sea cucumber, holothurian|
|330 | wood rabbit, cottontail, cottontail rabbit|
|331 | hare|
|332 | Angora, Angora rabbit|
|333 | hamster|
|334 | porcupine, hedgehog|
|335 | fox squirrel, eastern fox squirrel, Sciurus niger|
|336 | marmot|
|337 | beaver|
|338 | guinea pig, Cavia cobaya|
|339 | sorrel|
|340 | zebra|
|341 | hog, pig, grunter, squealer, Sus scrofa|
|342 | wild boar, boar, Sus scrofa|
|343 | warthog|
|344 | hippopotamus, hippo, river horse, Hippopotamus amphibius|
|345 | ox|
|346 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis|
|347 | bison|
|348 | ram, tup|
|349 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis|
|350 | ibex, Capra ibex|
|351 | hartebeest|
|352 | impala, Aepyceros melampus|
|353 | gazelle|
|354 | Arabian camel, dromedary, Camelus dromedarius|
|355 | llama|
|356 | weasel|
|357 | mink|
|358 | polecat, fitch, foulmart, foumart, Mustela putorius|
|359 | black-footed ferret, ferret, Mustela nigripes|
|360 | otter|
|361 | skunk, polecat, wood pussy|
|362 | badger|
|363 | armadillo|
|364 | three-toed sloth, ai, Bradypus tridactylus|
|365 | orangutan, orang, orangutang, Pongo pygmaeus|
|366 | gorilla, Gorilla gorilla|
|367 | chimpanzee, chimp, Pan troglodytes|
|368 | gibbon, Hylobates lar|
|369 | siamang, Hylobates syndactylus, Symphalangus syndactylus|
|370 | guenon, guenon monkey|
|371 | patas, hussar monkey, Erythrocebus patas|
|372 | baboon|
|373 | macaque|
|374 | langur|
|375 | colobus, colobus monkey|
|376 | proboscis monkey, Nasalis larvatus|
|377 | marmoset|
|378 | capuchin, ringtail, Cebus capucinus|
|379 | howler monkey, howler|
|380 | titi, titi monkey|
|381 | spider monkey, Ateles geoffroyi|
|382 | squirrel monkey, Saimiri sciureus|
|383 | Madagascar cat, ring-tailed lemur, Lemur catta|
|384 | indri, indris, Indri indri, Indri brevicaudatus|
|385 | Indian elephant, Elephas maximus|
|386 | African elephant, Loxodonta africana|
|387 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens|
|388 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca|
|389 | barracouta, snoek|
|390 | eel|
|391 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch|
|392 | rock beauty, Holocanthus tricolor|
|393 | anemone fish|
|394 | sturgeon|
|395 | gar, garfish, garpike, billfish, Lepisosteus osseus|
|396 | lionfish|
|397 | puffer, pufferfish, blowfish, globefish|
|398 | abacus|
|399 | abaya|
|400 | academic gown, academic robe, judge's robe|
|401 | accordion, piano accordion, squeeze box|
|402 | acoustic guitar|
|403 | aircraft carrier, carrier, flattop, attack aircraft carrier|
|404 | airliner|
|405 | airship, dirigible|
|406 | altar|
|407 | ambulance|
|408 | amphibian, amphibious vehicle|
|409 | analog clock|
|410 | apiary, bee house|
|411 | apron|
|412 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin|
|413 | assault rifle, assault gun|
|414 | backpack, back pack, knapsack, packsack, rucksack, haversack|
|415 | bakery, bakeshop, bakehouse|
|416 | balance beam, beam|
|417 | balloon|
|418 | ballpoint, ballpoint pen, ballpen, Biro|
|419 | Band Aid|
|420 | banjo|
|421 | bannister, banister, balustrade, balusters, handrail|
|422 | barbell|
|423 | barber chair|
|424 | barbershop|
|425 | barn|
|426 | barometer|
|427 | barrel, cask|
|428 | barrow, garden cart, lawn cart, wheelbarrow|
|429 | baseball|
|430 | basketball|
|431 | bassinet|
|432 | bassoon|
|433 | bathing cap, swimming cap|
|434 | bath towel|
|435 | bathtub, bathing tub, bath, tub|
|436 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon|
|437 | beacon, lighthouse, beacon light, pharos|
|438 | beaker|
|439 | bearskin, busby, shako|
|440 | beer bottle|
|441 | beer glass|
|442 | bell cote, bell cot|
|443 | bib|
|444 | bicycle-built-for-two, tandem bicycle, tandem|
|445 | bikini, two-piece|
|446 | binder, ring-binder|
|447 | binoculars, field glasses, opera glasses|
|448 | birdhouse|
|449 | boathouse|
|450 | bobsled, bobsleigh, bob|
|451 | bolo tie, bolo, bola tie, bola|
|452 | bonnet, poke bonnet|
|453 | bookcase|
|454 | bookshop, bookstore, bookstall|
|455 | bottlecap|
|456 | bow|
|457 | bow tie, bow-tie, bowtie|
|458 | brass, memorial tablet, plaque|
|459 | brassiere, bra, bandeau|
|460 | breakwater, groin, groyne, mole, bulwark, seawall, jetty|
|461 | breastplate, aegis, egis|
|462 | broom|
|463 | bucket, pail|
|464 | buckle|
|465 | bulletproof vest|
|466 | bullet train, bullet|
|467 | butcher shop, meat market|
|468 | cab, hack, taxi, taxicab|
|469 | caldron, cauldron|
|470 | candle, taper, wax light|
|471 | cannon|
|472 | canoe|
|473 | can opener, tin opener|
|474 | cardigan|
|475 | car mirror|
|476 | carousel, carrousel, merry-go-round, roundabout, whirligig|
|477 | carpenter's kit, tool kit|
|478 | carton|
|479 | car wheel|
|480 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM|
|481 | cassette|
|482 | cassette player|
|483 | castle|
|484 | catamaran|
|485 | CD player|
|486 | cello, violoncello|
|487 | cellular telephone, cellular phone, cellphone, cell, mobile phone|
|488 | chain|
|489 | chainlink fence|
|490 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour|
|491 | chain saw, chainsaw|
|492 | chest|
|493 | chiffonier, commode|
|494 | chime, bell, gong|
|495 | china cabinet, china closet|
|496 | Christmas stocking|
|497 | church, church building|
|498 | cinema, movie theater, movie theatre, movie house, picture palace|
|499 | cleaver, meat cleaver, chopper|
|500 | cliff dwelling|
|501 | cloak|
|502 | clog, geta, patten, sabot|
|503 | cocktail shaker|
|504 | coffee mug|
|505 | coffeepot|
|506 | coil, spiral, volute, whorl, helix|
|507 | combination lock|
|508 | computer keyboard, keypad|
|509 | confectionery, confectionary, candy store|
|510 | container ship, containership, container vessel|
|511 | convertible|
|512 | corkscrew, bottle screw|
|513 | cornet, horn, trumpet, trump|
|514 | cowboy boot|
|515 | cowboy hat, ten-gallon hat|
|516 | cradle|
|517 | crane_1|
|518 | crash helmet|
|519 | crate|
|520 | crib, cot|
|521 | Crock Pot|
|522 | croquet ball|
|523 | crutch|
|524 | cuirass|
|525 | dam, dike, dyke|
|526 | desk|
|527 | desktop computer|
|528 | dial telephone, dial phone|
|529 | diaper, nappy, napkin|
|530 | digital clock|
|531 | digital watch|
|532 | dining table, board|
|533 | dishrag, dishcloth|
|534 | dishwasher, dish washer, dishwashing machine|
|535 | disk brake, disc brake|
|536 | dock, dockage, docking facility|
|537 | dogsled, dog sled, dog sleigh|
|538 | dome|
|539 | doormat, welcome mat|
|540 | drilling platform, offshore rig|
|541 | drum, membranophone, tympan|
|542 | drumstick|
|543 | dumbbell|
|544 | Dutch oven|
|545 | electric fan, blower|
|546 | electric guitar|
|547 | electric locomotive|
|548 | entertainment center|
|549 | envelope|
|550 | espresso maker|
|551 | face powder|
|552 | feather boa, boa|
|553 | file, file cabinet, filing cabinet|
|554 | fireboat|
|555 | fire engine, fire truck|
|556 | fire screen, fireguard|
|557 | flagpole, flagstaff|
|558 | flute, transverse flute|
|559 | folding chair|
|560 | football helmet|
|561 | forklift|
|562 | fountain|
|563 | fountain pen|
|564 | four-poster|
|565 | freight car|
|566 | French horn, horn|
|567 | frying pan, frypan, skillet|
|568 | fur coat|
|569 | garbage truck, dustcart|
|570 | gasmask, respirator, gas helmet|
|571 | gas pump, gasoline pump, petrol pump, island dispenser|
|572 | goblet|
|573 | go-kart|
|574 | golf ball|
|575 | golfcart, golf cart|
|576 | gondola|
|577 | gong, tam-tam|
|578 | gown|
|579 | grand piano, grand|
|580 | greenhouse, nursery, glasshouse|
|581 | grille, radiator grille|
|582 | grocery store, grocery, food market, market|
|583 | guillotine|
|584 | hair slide|
|585 | hair spray|
|586 | half track|
|587 | hammer|
|588 | hamper|
|589 | hand blower, blow dryer, blow drier, hair dryer, hair drier|
|590 | hand-held computer, hand-held microcomputer|
|591 | handkerchief, hankie, hanky, hankey|
|592 | hard disc, hard disk, fixed disk|
|593 | harmonica, mouth organ, harp, mouth harp|
|594 | harp|
|595 | harvester, reaper|
|596 | hatchet|
|597 | holster|
|598 | home theater, home theatre|
|599 | honeycomb|
|600 | hook, claw|
|601 | hoopskirt, crinoline|
|602 | horizontal bar, high bar|
|603 | horse cart, horse-cart|
|604 | hourglass|
|605 | iPod|
|606 | iron, smoothing iron|
|607 | jack-o'-lantern|
|608 | jean, blue jean, denim|
|609 | jeep, landrover|
|610 | jersey, T-shirt, tee shirt|
|611 | jigsaw puzzle|
|612 | jinrikisha, ricksha, rickshaw|
|613 | joystick|
|614 | kimono|
|615 | knee pad|
|616 | knot|
|617 | lab coat, laboratory coat|
|618 | ladle|
|619 | lampshade, lamp shade|
|620 | laptop, laptop computer|
|621 | lawn mower, mower|
|622 | lens cap, lens cover|
|623 | letter opener, paper knife, paperknife|
|624 | library|
|625 | lifeboat|
|626 | lighter, light, igniter, ignitor|
|627 | limousine, limo|
|628 | liner, ocean liner|
|629 | lipstick, lip rouge|
|630 | Loafer|
|631 | lotion|
|632 | loudspeaker, speaker, speaker unit, loudspeaker system, speaker system|
|633 | loupe, jeweler's loupe|
|634 | lumbermill, sawmill|
|635 | magnetic compass|
|636 | mailbag, postbag|
|637 | mailbox, letter box|
|638 | maillot|
|639 | maillot, tank suit|
|640 | manhole cover|
|641 | maraca|
|642 | marimba, xylophone|
|643 | mask|
|644 | matchstick|
|645 | maypole|
|646 | maze, labyrinth|
|647 | measuring cup|
|648 | medicine chest, medicine cabinet|
|649 | megalith, megalithic structure|
|650 | microphone, mike|
|651 | microwave, microwave oven|
|652 | military uniform|
|653 | milk can|
|654 | minibus|
|655 | miniskirt, mini|
|656 | minivan|
|657 | missile|
|658 | mitten|
|659 | mixing bowl|
|660 | mobile home, manufactured home|
|661 | Model T|
|662 | modem|
|663 | monastery|
|664 | monitor|
|665 | moped|
|666 | mortar|
|667 | mortarboard|
|668 | mosque|
|669 | mosquito net|
|670 | motor scooter, scooter|
|671 | mountain bike, all-terrain bike, off-roader|
|672 | mountain tent|
|673 | mouse, computer mouse|
|674 | mousetrap|
|675 | moving van|
|676 | muzzle|
|677 | nail|
|678 | neck brace|
|679 | necklace|
|680 | nipple|
|681 | notebook, notebook computer|
|682 | obelisk|
|683 | oboe, hautboy, hautbois|
|684 | ocarina, sweet potato|
|685 | odometer, hodometer, mileometer, milometer|
|686 | oil filter|
|687 | organ, pipe organ|
|688 | oscilloscope, scope, cathode-ray oscilloscope, CRO|
|689 | overskirt|
|690 | oxcart|
|691 | oxygen mask|
|692 | packet|
|693 | paddle, boat paddle|
|694 | paddlewheel, paddle wheel|
|695 | padlock|
|696 | paintbrush|
|697 | pajama, pyjama, pj's, jammies|
|698 | palace|
|699 | panpipe, pandean pipe, syrinx|
|700 | paper towel|
|701 | parachute, chute|
|702 | parallel bars, bars|
|703 | park bench|
|704 | parking meter|
|705 | passenger car, coach, carriage|
|706 | patio, terrace|
|707 | pay-phone, pay-station|
|708 | pedestal, plinth, footstall|
|709 | pencil box, pencil case|
|710 | pencil sharpener|
|711 | perfume, essence|
|712 | Petri dish|
|713 | photocopier|
|714 | pick, plectrum, plectron|
|715 | pickelhaube|
|716 | picket fence, paling|
|717 | pickup, pickup truck|
|718 | pier|
|719 | piggy bank, penny bank|
|720 | pill bottle|
|721 | pillow|
|722 | ping-pong ball|
|723 | pinwheel|
|724 | pirate, pirate ship|
|725 | pitcher, ewer|
|726 | plane, carpenter's plane, woodworking plane|
|727 | planetarium|
|728 | plastic bag|
|729 | plate rack|
|730 | plow, plough|
|731 | plunger, plumber's helper|
|732 | Polaroid camera, Polaroid Land camera|
|733 | pole|
|734 | police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria|
|735 | poncho|
|736 | pool table, billiard table, snooker table|
|737 | pop bottle, soda bottle|
|738 | pot, flowerpot|
|739 | potter's wheel|
|740 | power drill|
|741 | prayer rug, prayer mat|
|742 | printer|
|743 | prison, prison house|
|744 | projectile, missile|
|745 | projector|
|746 | puck, hockey puck|
|747 | punching bag, punch bag, punching ball, punchball|
|748 | purse|
|749 | quill, quill pen|
|750 | quilt, comforter, comfort, puff|
|751 | racer, race car, racing car|
|752 | racket, racquet|
|753 | radiator|
|754 | radio, wireless|
|755 | radio telescope, radio reflector|
|756 | rain barrel|
|757 | recreational vehicle, RV, R.V.|
|758 | reel|
|759 | reflex camera|
|760 | refrigerator, icebox|
|761 | remote control, remote|
|762 | restaurant, eating house, eating place, eatery|
|763 | revolver, six-gun, six-shooter|
|764 | rifle|
|765 | rocking chair, rocker|
|766 | rotisserie|
|767 | rubber eraser, rubber, pencil eraser|
|768 | rugby ball|
|769 | rule, ruler|
|770 | running shoe|
|771 | safe|
|772 | safety pin|
|773 | saltshaker, salt shaker|
|774 | sandal|
|775 | sarong|
|776 | sax, saxophone|
|777 | scabbard|
|778 | scale, weighing machine|
|779 | school bus|
|780 | schooner|
|781 | scoreboard|
|782 | screen, CRT screen|
|783 | screw|
|784 | screwdriver|
|785 | seat belt, seatbelt|
|786 | sewing machine|
|787 | shield, buckler|
|788 | shoe shop, shoe-shop, shoe store|
|789 | shoji|
|790 | shopping basket|
|791 | shopping cart|
|792 | shovel|
|793 | shower cap|
|794 | shower curtain|
|795 | ski|
|796 | ski mask|
|797 | sleeping bag|
|798 | slide rule, slipstick|
|799 | sliding door|
|800 | slot, one-armed bandit|
|801 | snorkel|
|802 | snowmobile|
|803 | snowplow, snowplough|
|804 | soap dispenser|
|805 | soccer ball|
|806 | sock|
|807 | solar dish, solar collector, solar furnace|
|808 | sombrero|
|809 | soup bowl|
|810 | space bar|
|811 | space heater|
|812 | space shuttle|
|813 | spatula|
|814 | speedboat|
|815 | spider web, spider's web|
|816 | spindle|
|817 | sports car, sport car|
|818 | spotlight, spot|
|819 | stage|
|820 | steam locomotive|
|821 | steel arch bridge|
|822 | steel drum|
|823 | stethoscope|
|824 | stole|
|825 | stone wall|
|826 | stopwatch, stop watch|
|827 | stove|
|828 | strainer|
|829 | streetcar, tram, tramcar, trolley, trolley car|
|830 | stretcher|
|831 | studio couch, day bed|
|832 | stupa, tope|
|833 | submarine, pigboat, sub, U-boat|
|834 | suit, suit of clothes|
|835 | sundial|
|836 | sunglass|
|837 | sunglasses, dark glasses, shades|
|838 | sunscreen, sunblock, sun blocker|
|839 | suspension bridge|
|840 | swab, swob, mop|
|841 | sweatshirt|
|842 | swimming trunks, bathing trunks|
|843 | swing|
|844 | switch, electric switch, electrical switch|
|845 | syringe|
|846 | table lamp|
|847 | tank, army tank, armored combat vehicle, armoured combat vehicle|
|848 | tape player|
|849 | teapot|
|850 | teddy, teddy bear|
|851 | television, television system|
|852 | tennis ball|
|853 | thatch, thatched roof|
|854 | theater curtain, theatre curtain|
|855 | thimble|
|856 | thresher, thrasher, threshing machine|
|857 | throne|
|858 | tile roof|
|859 | toaster|
|860 | tobacco shop, tobacconist shop, tobacconist|
|861 | toilet seat|
|862 | torch|
|863 | totem pole|
|864 | tow truck, tow car, wrecker|
|865 | toyshop|
|866 | tractor|
|867 | trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi|
|868 | tray|
|869 | trench coat|
|870 | tricycle, trike, velocipede|
|871 | trimaran|
|872 | tripod|
|873 | triumphal arch|
|874 | trolleybus, trolley coach, trackless trolley|
|875 | trombone|
|876 | tub, vat|
|877 | turnstile|
|878 | typewriter keyboard|
|879 | umbrella|
|880 | unicycle, monocycle|
|881 | upright, upright piano|
|882 | vacuum, vacuum cleaner|
|883 | vase|
|884 | vault|
|885 | velvet|
|886 | vending machine|
|887 | vestment|
|888 | viaduct|
|889 | violin, fiddle|
|890 | volleyball|
|891 | waffle iron|
|892 | wall clock|
|893 | wallet, billfold, notecase, pocketbook|
|894 | wardrobe, closet, press|
|895 | warplane, military plane|
|896 | washbasin, handbasin, washbowl, lavabo, wash-hand basin|
|897 | washer, automatic washer, washing machine|
|898 | water bottle|
|899 | water jug|
|900 | water tower|
|901 | whiskey jug|
|902 | whistle|
|903 | wig|
|904 | window screen|
|905 | window shade|
|906 | Windsor tie|
|907 | wine bottle|
|908 | wing|
|909 | wok|
|910 | wooden spoon|
|911 | wool, woolen, woollen|
|912 | worm fence, snake fence, snake-rail fence, Virginia fence|
|913 | wreck|
|914 | yawl|
|915 | yurt|
|916 | web site, website, internet site, site|
|917 | comic book|
|918 | crossword puzzle, crossword|
|919 | street sign|
|920 | traffic light, traffic signal, stoplight|
|921 | book jacket, dust cover, dust jacket, dust wrapper|
|922 | menu|
|923 | plate|
|924 | guacamole|
|925 | consomme|
|926 | hot pot, hotpot|
|927 | trifle|
|928 | ice cream, icecream|
|929 | ice lolly, lolly, lollipop, popsicle|
|930 | French loaf|
|931 | bagel, beigel|
|932 | pretzel|
|933 | cheeseburger|
|934 | hotdog, hot dog, red hot|
|935 | mashed potato|
|936 | head cabbage|
|937 | broccoli|
|938 | cauliflower|
|939 | zucchini, courgette|
|940 | spaghetti squash|
|941 | acorn squash|
|942 | butternut squash|
|943 | cucumber, cuke|
|944 | artichoke, globe artichoke|
|945 | bell pepper|
|946 | cardoon|
|947 | mushroom|
|948 | Granny Smith|
|949 | strawberry|
|950 | orange|
|951 | lemon|
|952 | fig|
|953 | pineapple, ananas|
|954 | banana|
|955 | jackfruit, jak, jack|
|956 | custard apple|
|957 | pomegranate|
|958 | hay|
|959 | carbonara|
|960 | chocolate sauce, chocolate syrup|
|961 | dough|
|962 | meat loaf, meatloaf|
|963 | pizza, pizza pie|
|964 | potpie|
|965 | burrito|
|966 | red wine|
|967 | espresso|
|968 | cup|
|969 | eggnog|
|970 | alp|
|971 | bubble|
|972 | cliff, drop, drop-off|
|973 | coral reef|
|974 | geyser|
|975 | lakeside, lakeshore|
|976 | promontory, headland, head, foreland|
|977 | sandbar, sand bar|
|978 | seashore, coast, seacoast, sea-coast|
|979 | valley, vale|
|980 | volcano|
|981 | ballplayer, baseball player|
|982 | groom, bridegroom|
|983 | scuba diver|
|984 | rapeseed|
|985 | daisy|
|986 | yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum|
|987 | corn|
|988 | acorn|
|989 | hip, rose hip, rosehip|
|990 | buckeye, horse chestnut, conker|
|991 | coral fungus|
|992 | agaric|
|993 | gyromitra|
|994 | stinkhorn, carrion fungus|
|995 | earthstar|
|996 | hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa|
|997 | bolete|
|998 | ear, spike, capitulum|
|999 | toilet tissue, toilet paper, bathroom tissue|
</details>
### Data Splits
| |train |validation| test |
|-------------|------:|---------:|------:|
|# of examples|1281167|50000 |100000 |
## Dataset Creation
### Curation Rationale
The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.
### Source Data
#### Initial Data Collection and Normalization
Initial data for ImageNet image classification task consists of photographs collected from [Flickr](https://www.flickr.com) and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs [1](https://ieeexplore.ieee.org/abstract/document/5206848). The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.
#### Who are the source language producers?
WordNet synsets further quality controlled by human annotators. The images are from Flickr.
### Annotations
#### Annotation process
The annotation process of collecting ImageNet for image classification task is a three step process.
1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
1. Collecting the candidate image for these object categories using a search engine.
1. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.
See the section 3.1 in [1](https://arxiv.org/abs/1409.0575) for more details on data collection procedure and [2](https://ieeexplore.ieee.org/abstract/document/5206848) for general information on ImageNet.
#### Who are the annotators?
Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See [1](https://arxiv.org/abs/1409.0575) for more details.
### Personal and Sensitive Information
The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](https://image-net.org/face-obfuscation/) on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been [an attempt](https://arxiv.org/abs/1912.07726) at filtering and balancing the people subtree in the larger ImageNet.
## Considerations for Using the Data
### Social Impact of Dataset
The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in [1](https://arxiv.org/abs/1409.0575) for a discussion on social impact of the dataset.
### Discussion of Biases
1. A [study](https://image-net.org/update-sep-17-2019.php) of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
1. A [study](https://arxiv.org/abs/1811.12231) has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
1. Another [study](https://arxiv.org/abs/2109.13228) more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
1. ImageNet data with face obfuscation is also provided at [this link](https://image-net.org/face-obfuscation/)
1. A study on genealogy of ImageNet is can be found at [this link](https://journals.sagepub.com/doi/full/10.1177/20539517211035955) about the "norms, values, and assumptions" in ImageNet.
1. See [this study](https://arxiv.org/abs/1912.07726) on filtering and balancing the distribution of people subtree in the larger complete ImageNet.
### Other Known Limitations
1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](https://arxiv.org/abs/2109.13228) [[2]](https://arxiv.org/abs/1409.0575) [[3]](https://ieeexplore.ieee.org/abstract/document/5206848).
## Additional Information
### Dataset Curators
Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848):
- Olga Russakovsky
- Jia Deng
- Hao Su
- Jonathan Krause
- Sanjeev Satheesh
- Wei Dong
- Richard Socher
- Li-Jia Li
- Kai Li
- Sean Ma
- Zhiheng Huang
- Andrej Karpathy
- Aditya Khosla
- Michael Bernstein
- Alexander C Berg
- Li Fei-Fei
### Licensing Information
In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
1. The law of the State of New Jersey shall apply to all disputes under this agreement.
### Citation Information
```bibtex
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset. | imagenet-1k | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1409.0575",
"arxiv:1912.07726",
"arxiv:1811.12231",
"arxiv:2109.13228",
"region:us"
] | 2022-05-02T15:33:23+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "license_details": "imagenet-agreement", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "test", "num_bytes": 13613661561, "num_examples": 100000}, {"name": "train", "num_bytes": 146956944242, "num_examples": 1281167}, {"name": "validation", "num_bytes": 6709003386, "num_examples": 50000}], "download_size": 166009941208, "dataset_size": 167279609189}} | 2023-12-21T20:27:21+00:00 |
e387ddf167f3eba99936cff89909ed6264f17e1f |
# Dataset Card for Czech Subjectivity Dataset
### Dataset Summary
Czech subjectivity dataset (Subj-CS) of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. See the paper description https://arxiv.org/abs/2204.13915
### Github
https://github.com/pauli31/czech-subjectivity-dataset
### Supported Tasks and Leaderboards
Subjectivity Analysis
### Languages
Czech
### Data Instances
train/dev/test
### Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.](https://creativecommons.org/licenses/by-nc-sa/4.0/)
### Citation Information
If you use our dataset or software for academic research, please cite the our [paper](https://arxiv.org/abs/2204.13915)
```
@article{pib2022czech,
title={Czech Dataset for Cross-lingual Subjectivity Classification},
author={Pavel Přibáň and Josef Steinberger},
year={2022},
eprint={2204.13915},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contact
pribanp@kiv.zcu.cz
### Contributions
Thanks to [@pauli31](https://github.com/pauli31) for adding this dataset. | pauli31/czech-subjectivity-dataset | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:cs",
"license:cc-by-nc-sa-4.0",
"arxiv:2204.13915",
"region:us"
] | 2022-05-02T17:27:17+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["cs"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "Czech Subjectivity Dataset"} | 2024-01-05T20:05:40+00:00 |
d2d59a4ccf942da4f70948219362271f14efc5c8 | arbml/masader | [
"license:mit",
"region:us"
] | 2022-05-03T00:45:10+00:00 | {"license": "mit"} | 2022-07-08T13:45:05+00:00 |
|
143c5879a8ddd8a45a5ed7c7f429c89b430a80c6 | Apo/ADE20K_sky_13labels | [
"license:afl-3.0",
"region:us"
] | 2022-05-03T06:24:40+00:00 | {"license": "afl-3.0"} | 2022-05-03T06:24:40+00:00 |
|
0d01b67c73ed0a50d03a46e8283b37dab58fe3bc | Erwin/coffee_reviews_corpus | [
"license:mit",
"region:us"
] | 2022-05-03T07:22:38+00:00 | {"license": "mit"} | 2022-05-03T07:22:38+00:00 |
|
ffd50dace84cd36afc994d9193d988b30c200a5b |
# Dataset Card for Text-based NP Enrichment
## 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://yanaiela.github.io/TNE/
- **Repository:** https://github.com/yanaiela/TNE
- **Paper:** https://arxiv.org/abs/2109.12085
- **Leaderboard:** [TNE OOD](https://leaderboard.allenai.org/tne-ood/submissions/public)
[TNE](https://leaderboard.allenai.org/tne/submissions/public)
- **Point of Contact:** [Yanai Elazar](mailto:yanaiela@gmail.com)
### Dataset Summary
Text-based NP Enrichment (TNE) is a natural language understanding (NLU) task, which focus on relations between noun phrases (NPs) that can be mediated via prepositions. The dataset contains 5,497 documents, annotated exhaustively with all possible links between the NPs in each document.
The main data comes from WikiNews, which is used for train/dev/test. We also collected an additional set of 509 documents to serve as out of distribution (OOD) data points, from the Book Corpus, IMDB reviews and Reddit.
### Supported Tasks and Leaderboards
The data contain both the main data for the TNE task, as well as coreference resolution data.
There are two leaderboards for the TNE data, one for the standard test set, and another one for the OOD test set:
- [TNE Leaderboard](https://leaderboard.allenai.org/tne/submissions/public)
- [TNE OOD Leaderboard](https://leaderboard.allenai.org/tne-ood/submissions/public)
### Languages
The text in the dataset is in English, as spoken in the different domains we include. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
The original files are in a jsonl format, containing a dictionary of a single document, in each line.
Each document contain a different amount of labels, due to the different amount of NPs.
The test and ood splits come without the annotated labels.
### Data Fields
A document consists of:
* `id`: a unique identifier of a document, beginning with `r` and followed by a number
* `text`: the text of the document. The title and subtitles (if exists) are separated with two new lines. The paragraphs
are separated by a single new line.
* `tokens`: a list of string, containing the tokenized tokens
* `nps`: a list of dictionaries, containing the following entries:
* `text`: the text of the np
* `start_index`: an integer indicating the starting index in the text
* `end_index`: an integer indicating the ending index in the text
* `start_token`: an integer indicating the first token of the np out of the tokenized tokens
* `end_token`: an integer indicating the last token of the np out of the tokenized tokens
* `id`: the id of the np
* `np_relations`: these are the relation labels of the document. It is a list of dictionaries, where each
dictionary contains:
* `anchor`: the id of the anchor np
* `complement`: the id of the complement np
* `preposition`: the preposition that links between the anchor and the complement. This can take one out of 24 pre-defined preposition (23 + member(s)-of)
* `complement_coref_cluster_id`: the coreference id, which the complement is part of.
* `coref`: the coreference labels. It contains a list of dictionaries, where each dictionary contains:
* `id`: the id of the coreference cluster
* `members`: the ids of the nps members of such cluster
* `np_type`: the type of cluster. It can be either
* `standard`: regular coreference cluster
* `time/date/measurement`: a time / date / measurement np. These will be singletons.
* `idiomatic`: an idiomatic expression
* `metadata`: metadata of the document. It contains the following:
* `annotators`: a dictionary with anonymized annotators id
* `coref_worker`: the coreference worker id
* `consolidator_worker`: the consolidator worker id
* `np-relations_worker`: the np relations worker id
* `url`: the url where the document was taken from (not always existing)
* `source`: the original file name where the document was taken from
### Data Splits
The dataset is spread across four files, for the four different splits: train, dev, test and test_ood.
Additional details on the data statistics can be found in the [paper](https://arxiv.org/abs/2109.12085)
## Dataset Creation
### Curation Rationale
TNE was build as a new task for language understanding, focusing on extracting relations between nouns, moderated by prepositions.
### 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
The dataset was created by Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty, during work done at Bar-Ilan University, and AI2.
### Licensing Information
The data is released under the MIT license.
### Citation Information
```bibtex
@article{tne,
author = {Elazar, Yanai and Basmov, Victoria and Goldberg, Yoav and Tsarfaty, Reut},
title = "{Text-based NP Enrichment}",
journal = {Transactions of the Association for Computational Linguistics},
year = {2022},
}
```
### Contributions
Thanks to [@yanaiela](https://github.com/yanaiela), who is also the first author of the paper, for adding this dataset. | tne | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2109.12085",
"region:us"
] | 2022-05-03T13:06:32+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "Text-based NP Enrichment", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "nps", "list": [{"name": "text", "dtype": "string"}, {"name": "first_char", "dtype": "int32"}, {"name": "last_char", "dtype": "int32"}, {"name": "first_token", "dtype": "int32"}, {"name": "last_token", "dtype": "int32"}, {"name": "id", "dtype": "string"}]}, {"name": "np_relations", "list": [{"name": "anchor", "dtype": "string"}, {"name": "complement", "dtype": "string"}, {"name": "preposition", "dtype": {"class_label": {"names": {"0": "about", "1": "for", "2": "with", "3": "from", "4": "among", "5": "by", "6": "on", "7": "at", "8": "during", "9": "of", "10": "member(s) of", "11": "in", "12": "after", "13": "under", "14": "to", "15": "into", "16": "before", "17": "near", "18": "outside", "19": "around", "20": "between", "21": "against", "22": "over", "23": "inside"}}}}, {"name": "complement_coref_cluster_id", "dtype": "string"}]}, {"name": "coref", "list": [{"name": "id", "dtype": "string"}, {"name": "members", "sequence": "string"}, {"name": "np_type", "dtype": {"class_label": {"names": {"0": "standard", "1": "time/date/measurement", "2": "idiomatic"}}}}]}, {"name": "metadata", "struct": [{"name": "annotators", "struct": [{"name": "coref_worker", "dtype": "int32"}, {"name": "consolidator_worker", "dtype": "int32"}, {"name": "np-relations_worker", "sequence": "int32"}]}, {"name": "url", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 41308170, "num_examples": 3988}, {"name": "validation", "num_bytes": 5495419, "num_examples": 500}, {"name": "test", "num_bytes": 2203716, "num_examples": 500}, {"name": "test_ood", "num_bytes": 2249352, "num_examples": 509}], "download_size": 14194578, "dataset_size": 51256657}} | 2024-01-18T11:19:07+00:00 |
8b6e3f5b1dbd7244ae8251f5b1dc02da9146a04b |
# esCorpius Multilingual Raw
In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, they present important shortcomings for languages different from English, as they are either too small, or present a low quality derived from sub-optimal cleaning and deduplication. In this repository, we introduce esCorpius-m, a multilingual crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in some of the languages covered with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius-m has been released under CC BY-NC-ND 4.0 license.
# Usage
```
dataset = load_dataset('LHF/escorpius-m', split='train', streaming=True)
```
# Intended use
This corpus is the *raw version* of the esCorpius-m corpus. This corpus can be used for benchmarking deduplication tools.
## Other corpora
- esCorpius multilingual corpus (deduplicated): https://huggingface.co/datasets/LHF/escorpius-m
- esCorpius original *Spanish-only* corpus (deduplicated): https://huggingface.co/datasets/LHF/escorpius
## Citation
Link to paper: https://www.isca-speech.org/archive/pdfs/iberspeech_2022/gutierrezfandino22_iberspeech.pdf / https://arxiv.org/abs/2206.15147
Cite this work:
```
@inproceedings{gutierrezfandino22_iberspeech,
author={Asier Gutiérrez-Fandiño and David Pérez-Fernández and Jordi Armengol-Estapé and David Griol and Zoraida Callejas},
title={{esCorpius: A Massive Spanish Crawling Corpus}},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
year=2022,
booktitle={Proc. IberSPEECH 2022},
pages={126--130},
doi={10.21437/IberSPEECH.2022-26}
}
```
## Disclaimer
We did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus.
| LHF/escorpius-mr | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:multilingual",
"size_categories:100B<n<1T",
"source_datasets:original",
"language:af",
"language:ar",
"language:bn",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:gl",
"language:hi",
"language:hr",
"language:it",
"language:ja",
"language:ko",
"language:mt",
"language:nl",
"language:no",
"language:oc",
"language:pa",
"language:pl",
"language:pt",
"language:ro",
"language:sl",
"language:sr",
"language:sv",
"language:tr",
"language:uk",
"language:ur",
"license:cc-by-nc-nd-4.0",
"arxiv:2206.15147",
"region:us"
] | 2022-05-03T17:49:47+00:00 | {"language": ["af", "ar", "bn", "ca", "cs", "da", "de", "el", "eu", "fa", "fi", "fr", "gl", "hi", "hr", "it", "ja", "ko", "mt", "nl", false, "oc", "pa", "pl", "pt", "ro", "sl", "sr", "sv", "tr", "uk", "ur"], "license": "cc-by-nc-nd-4.0", "multilinguality": ["multilingual"], "size_categories": ["100B<n<1T"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"]} | 2023-05-11T21:29:21+00:00 |
e874dfff456d716a0700c244c36baebd58581ebd | farazeftekhar/geojson | [
"license:other",
"region:us"
] | 2022-05-03T18:12:53+00:00 | {"license": "other"} | 2022-05-03T18:15:48+00:00 |
|
e68446ffa2df3054a86fdb23cf798182477bd13e | andstor/smart_contract_code_comments | [
"region:us"
] | 2022-05-03T20:13:32+00:00 | {"paperswithcode_id": "verified-smart-contract-code-comments", "dataset_info": {"features": [{"name": "contract_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "contract_address", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "class_name", "dtype": "string"}, {"name": "class_code", "dtype": "string"}, {"name": "class_documentation", "dtype": "string"}, {"name": "class_documentation_type", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "func_code", "dtype": "string"}, {"name": "func_documentation", "dtype": "string"}, {"name": "func_documentation_type", "dtype": "string"}, {"name": "compiler_version", "dtype": "string"}, {"name": "license_type", "dtype": "string"}, {"name": "swarm_source", "dtype": "string"}, {"name": "meta", "struct": [{"name": "func_code_index", "sequence": "int64"}]}, {"name": "__index_level_0__", "dtype": "int64"}], "config_name": "data", "splits": [{"name": "train", "num_bytes": 11530607173, "num_examples": 1267441}, {"name": "test", "num_bytes": 1306082431, "num_examples": 143080}, {"name": "validation", "num_bytes": 1264266873, "num_examples": 130849}], "download_size": 1995835391, "dataset_size": 14100956477}} | 2023-09-20T12:40:03+00:00 |
|
77840f2f84038fdf4b608fff764b21b7ef18eb34 |
# Dataset Card for Elsevier OA CC-By
## Table of Contents
- [Dataset Card for Elsevier OA CC-By](#dataset-card-for-elsevier-oa-cc-by)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs
- **Repository:** https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs
- **Paper:** https://arxiv.org/abs/2008.00774
- **Leaderboard:**
- **Point of Contact:** [@orieg](https://huggingface.co/orieg)
### Dataset Summary
Elsevier OA CC-By: This is a corpus of 40k (40,091) open access (OA) CC-BY articles from across Elsevier’s journals
representing a large scale, cross-discipline set of research data to support NLP and ML research. The corpus include full-text
articles published in 2014 to 2020 and are categorized in 27 Mid Level ASJC Code (subject classification).
***Distribution of Publication Years***
| Publication Year | Number of Articles |
| :---: | :---: |
| 2014 | 3018 |
| 2015 | 4438 |
| 2016 | 5913 |
| 2017 | 6419 |
| 2018 | 8016 |
| 2019 | 10135 |
| 2020 | 2159 |
***Distribution of Articles Per Mid Level ASJC Code. Each article can belong to multiple ASJC codes.***
| Discipline | Count |
| --- | ---: |
| General | 3847 |
| Agricultural and Biological Sciences | 4840 |
| Arts and Humanities | 982 |
| Biochemistry, Genetics and Molecular Biology | 8356 |
| Business, Management and Accounting | 937 |
| Chemical Engineering | 1878 |
| Chemistry | 2490 |
| Computer Science | 2039 |
| Decision Sciences | 406 |
| Earth and Planetary Sciences | 2393 |
| Economics, Econometrics and Finance | 976 |
| Energy | 2730 |
| Engineering | 4778 |
| Environmental Science | 6049 |
| Immunology and Microbiology | 3211 |
| Materials Science | 3477 |
| Mathematics | 538 |
| Medicine | 7273 |
| Neuroscience | 3669 |
| Nursing | 308 |
| Pharmacology, Toxicology and Pharmaceutics | 2405 |
| Physics and Astronomy | 2404 |
| Psychology | 1760 |
| Social Sciences | 3540 |
| Veterinary | 991 |
| Dentistry | 40 |
| Health Professions | 821 |
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## Dataset Structure
### Data Instances
The original dataset was published with the following json structure:
```
{
"docId": <str>,
"metadata":{
"title": <str>,
"authors": [
{
"first": <str>,
"initial": <str>,
"last": <str>,
"email": <str>
},
...
],
"issn": <str>,
"volume": <str>,
"firstpage": <str>,
"lastpage": <str>,
"pub_year": <int>,
"doi": <str>,
"pmid": <str>,
"openaccess": "Full",
"subjareas": [<str>],
"keywords": [<str>],
"asjc": [<int>],
},
"abstract":[
{
"sentence": <str>,
"startOffset": <int>,
"endOffset": <int>
},
...
],
"bib_entries":{
"BIBREF0":{
"title":<str>,
"authors":[
{
"last":<str>,
"initial":<str>,
"first":<str>
},
...
],
"issn": <str>,
"volume": <str>,
"firstpage": <str>,
"lastpage": <str>,
"pub_year": <int>,
"doi": <str>,
"pmid": <str>
},
...
},
"body_text":[
{
"sentence": <str>,
"secId": <str>,
"startOffset": <int>,
"endOffset": <int>,
"title": <str>,
"refoffsets": {
<str>:{
"endOffset":<int>,
"startOffset":<int>
}
},
"parents": [
{
"id": <str>,
"title": <str>
},
...
]
},
...
]
}
```
***docId*** The docID is the identifier of the document. This is unique to the document, and can be resolved into a URL
for the document through the addition of `https//www.sciencedirect.com/science/pii/<docId>`
***abstract*** This is the author provided abstract for the document
***body_text*** The full text for the document. The text has been split on sentence boundaries, thus making it easier to
use across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and
IDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the
title of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article
structure. References have been extracted from the sentences. The IDs of the extracted reference and their respective
offset within the sentence can be found in the “refoffsets” field. The complete list of references are can be found in
the “bib_entry” field along with the references’ respective metadata. Some will be missing as we only keep ‘clean’
sentences,
***bib_entities*** All the references from within the document can be found in this section. If the meta data for the
reference is available, it has been added against the key for the reference. Where possible information such as the
document titles, authors, and relevant identifiers (DOI and PMID) are included. The keys for each reference can be
found in the sentence where the reference is used with the start and end offset of where in the sentence that reference
was used.
***metadata*** Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and
PMID). Along with a number of classification schemes such as ASJC and Subject Classification.
***author_highlights*** Author highlights were included in the corpus where the author(s) have provided them. The
coverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with
the aim of summarising the core findings and results in the article.
### Data Fields
* ***title***: This is the author provided title for the document. 100% coverage.
* ***abstract***: This is the author provided abstract for the document. 99.25% coverage.
* ***keywords***: This is the author and publisher provided keywords for the document. 100% coverage.
* ***asjc***: This is the disciplines for the document as represented by 334 ASJC (All Science Journal Classification) codes. 100% coverage.
* ***subjareas***: This is the Subject Classification for the document as represented by 27 ASJC top-level subject classifications. 100% coverage.
* ***body_text***: The full text for the document. 100% coverage.
* ***author_highlights***: This is the author provided highlights for the document. 61.31% coverage.
### Data Splits
***Distribution of Publication Years***
| | Train | Test | Validation |
| --- | :---: | :---: | :---: |
| All Articles | 32072 | 4009 | 4008 |
| With Author Highlights | 19644 | 2420 | 2514 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Date the data was collected: 2020-06-25T11:00:00.000Z
See the [original paper](https://doi.org/10.48550/arXiv.2008.00774) for more detail on the data collection process.
#### Who are the source language producers?
See `3.1 Data Sampling` in the [original paper](https://doi.org/10.48550/arXiv.2008.00774).
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@article{Kershaw2020ElsevierOC,
title = {Elsevier OA CC-By Corpus},
author = {Daniel James Kershaw and R. Koeling},
journal = {ArXiv},
year = {2020},
volume = {abs/2008.00774},
doi = {https://doi.org/10.48550/arXiv.2008.00774},
url = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs},
keywords = {Science, Natural Language Processing, Machine Learning, Open Dataset},
abstract = {We introduce the Elsevier OA CC-BY corpus. This is the first open
corpus of Scientific Research papers which has a representative sample
from across scientific disciplines. This corpus not only includes the
full text of the article, but also the metadata of the documents,
along with the bibliographic information for each reference.}
}
```
```
@dataset{https://10.17632/zm33cdndxs.3,
doi = {10.17632/zm33cdndxs.2},
url = {https://data.mendeley.com/datasets/zm33cdndxs/3},
author = "Daniel Kershaw and Rob Koeling",
keywords = {Science, Natural Language Processing, Machine Learning, Open Dataset},
title = {Elsevier OA CC-BY Corpus},
publisher = {Mendeley},
year = {2020},
month = {sep}
}
```
### Contributions
Thanks to [@orieg](https://github.com/orieg) for adding this dataset. | orieg/elsevier-oa-cc-by | [
"task_categories:fill-mask",
"task_categories:summarization",
"task_categories:text-classification",
"task_ids:masked-language-modeling",
"task_ids:news-articles-summarization",
"task_ids:news-articles-headline-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2008.00774",
"region:us"
] | 2022-05-03T21:13:33+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["fill-mask", "summarization", "text-classification"], "task_ids": ["masked-language-modeling", "news-articles-summarization", "news-articles-headline-generation"], "paperswithcode_id": "elsevier-oa-cc-by", "pretty_name": "Elsevier OA CC-By"} | 2022-07-01T14:59:58+00:00 |
8092b28c4f8490d234c1385bb385575ec7408330 | allenai/drug-combo-extraction | [
"license:mit",
"region:us"
] | 2022-05-04T02:26:01+00:00 | {"license": "mit"} | 2022-05-04T03:12:53+00:00 |
|
aee7fea371b991a01db75877fd23e37d381379c0 |
# Dataset Card for Ukhushn/home-depot
| Ukhushn/home-depot | [
"task_categories:sentence-similarity",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:afl-3.0",
"region:us"
] | 2022-05-04T03:13:06+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["afl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["sentence-similarity"], "task_ids": [], "pretty_name": "Ukhushn/home-depot", "language_bcp47": ["en-US"]} | 2022-10-25T09:20:53+00:00 |
ad1898d0b872d98ab4747b07315385a1736ce36b | This is used for the pix2seq model. It contains the preprocessed pixel values of the cats image, useful for converting. | nielsr/test | [
"region:us"
] | 2022-05-04T10:28:43+00:00 | {} | 2022-05-04T11:09:13+00:00 |
fc36ebb3fa15bdf8731049b609e61d1fb5e696a5 | nlpconnect/DocVQA | [
"license:apache-2.0",
"region:us"
] | 2022-05-04T13:24:06+00:00 | {"license": "apache-2.0"} | 2022-05-04T13:24:06+00:00 |
|
30a47cfa557c996f64903f01250799d453be6215 |
# Dataset Card for TextVQA
## 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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://textvqa.org
- **Repository:** https://github.com/facebookresearch/mmf
- **Paper:** https://arxiv.org/abs/1904.08920
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/874/overview
- **Point of Contact:** mailto:amanpreet@nyu.edu
### Dataset Summary
TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset. The dataset uses [VQA accuracy](https://visualqa.org/evaluation.html) metric for evaluation.
### Supported Tasks and Leaderboards
- `visual-question-answering`: The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image.
### Languages
The questions in the dataset are in English.
## Dataset Structure
### Data Instances
A typical sample mainly contains the question in `question` field, an image object in `image` field, OpenImage image id in `image_id` and lot of other useful metadata. 10 answers per questions are contained in the `answers` attribute. For test set, 10 empty strings are contained in the `answers` field as the answers are not available for it.
An example look like below:
```
{'question': 'who is this copyrighted by?',
'image_id': '00685bc495504d61',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
'image_classes': ['Vehicle', 'Tower', 'Airplane', 'Aircraft'],
'flickr_original_url': 'https://farm2.staticflickr.com/5067/5620759429_4ea686e643_o.jpg',
'flickr_300k_url': 'https://c5.staticflickr.com/6/5067/5620759429_f43a649fb5_z.jpg',
'image_width': 786,
'image_height': 1024,
'answers': ['simon clancy',
'simon ciancy',
'simon clancy',
'simon clancy',
'the brand is bayard',
'simon clancy',
'simon clancy',
'simon clancy',
'simon clancy',
'simon clancy'],
'question_tokens': ['who', 'is', 'this', 'copyrighted', 'by'],
'question_id': 3,
'set_name': 'train'
},
```
### Data Fields
- `question`: string, the question that is being asked about the image
- `image_id`: string, id of the image which is same as the OpenImages id
- `image`: A `PIL.Image.Image` object containing the image about which the question is being asked. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `image_classes`: List[str], The OpenImages classes to which the image belongs to.
- `flickr_original_url`: string, URL to original image on Flickr
- `flickr_300k_url`: string, URL to resized and low-resolution image on Flickr.
- `image_width`: int, Width of the original image.
- `image_height`: int, Height of the original image.
- `question_tokens`: List[str], A pre-tokenized list of question.
- `answers`: List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers.
- `question_id`: int, Unique id of the question.
- `set_name`: string, the set to which this question belongs.
### Data Splits
There are three splits. `train`, `validation` and `test`. The `train` and `validation` sets share images with OpenImages `train` set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on `test` set, you need to go to the [EvalAI leaderboard](https://eval.ai/web/challenges/challenge-page/874/overview) and upload your predictions there. Please see instructions at [https://textvqa.org/challenge/](https://textvqa.org/challenge/).
## Dataset Creation
### Curation Rationale
From the paper:
> Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer.
### Source Data
#### Initial Data Collection and Normalization
The initial images were sourced from [OpenImages](https://storage.googleapis.com/openimages/web/factsfigures_v4.html) v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See [annotation process](#annotation-process) section to understand the next stages.
#### Who are the source language producers?
English Crowdsource Annotators
### Annotations
#### Annotation process
After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage.
#### Who are the annotators?
Annotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper.
### Personal and Sensitive Information
The dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided `image_classes` data field is one option to try to filter out some of this information.
## Considerations for Using the Data
### Social Impact of Dataset
The paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including [TextCaps](https://textvqa.org/textcaps) and [TextOCR](https://textvqa.org/textocr). Similar datasets were introduced over the time which specifically focus on sight-disabled users such as [VizWiz](https://vizwiz.org) or focusing specifically on the same problem as TextVQA like [STVQA](https://paperswithcode.com/dataset/st-vqa), [DocVQA](https://arxiv.org/abs/2007.00398v3) and [OCRVQA](https://ocr-vqa.github.io/). Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets.
### Discussion of Biases
Question-only bias where a model is able to answer the question without even looking at the image is discussed in the [paper](https://arxiv.org/abs/1904.08920) which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric.
### Other Known Limitations
- The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding.
- The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers.
- The metric used for calculating accuracy is same as [VQA accuracy](https://visualqa.org/evaluation.html). This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR.
## Additional Information
### Dataset Curators
- [Amanpreet Singh](https://github.com/apsdehal)
- Vivek Natarjan
- Meet Shah
- Yu Jiang
- Xinlei Chen
- Dhruv Batra
- Devi Parikh
- Marcus Rohrbach
### Licensing Information
CC by 4.0
### Citation Information
```bibtex
@inproceedings{singh2019towards,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8317-8326},
year={2019}
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset. | textvqa | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1904.08920",
"arxiv:2007.00398",
"region:us"
] | 2022-05-05T05:44:56+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["visual-question-answering"], "task_ids": ["visual-question-answering"], "pretty_name": "TextVQA", "dataset_info": [{"config_name": "train", "features": [{"name": "image_id", "dtype": "string"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "question_tokens", "sequence": "string"}, {"name": "image", "dtype": "image"}, {"name": "image_width", "dtype": "int32"}, {"name": "image_height", "dtype": "int32"}, {"name": "flickr_original_url", "dtype": "string"}, {"name": "flickr_300k_url", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "image_classes", "sequence": "string"}, {"name": "set_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21381310, "num_examples": 34602}, {"name": "validation", "num_bytes": 3077854, "num_examples": 5000}, {"name": "test", "num_bytes": 3025046, "num_examples": 5734}], "download_size": 8070116310, "dataset_size": 27484210}, {"config_name": "val", "features": [{"name": "image_id", "dtype": "string"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "question_tokens", "sequence": "string"}, {"name": "image", "dtype": "image"}, {"name": "image_width", "dtype": "int32"}, {"name": "image_height", "dtype": "int32"}, {"name": "flickr_original_url", "dtype": "string"}, {"name": "flickr_300k_url", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "image_classes", "sequence": "string"}, {"name": "set_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21381310, "num_examples": 34602}, {"name": "validation", "num_bytes": 3077854, "num_examples": 5000}, {"name": "test", "num_bytes": 3025046, "num_examples": 5734}], "download_size": 8070116310, "dataset_size": 27484210}, {"config_name": "test", "features": [{"name": "image_id", "dtype": "string"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "question_tokens", "sequence": "string"}, {"name": "image", "dtype": "image"}, {"name": "image_width", "dtype": "int32"}, {"name": "image_height", "dtype": "int32"}, {"name": "flickr_original_url", "dtype": "string"}, {"name": "flickr_300k_url", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "image_classes", "sequence": "string"}, {"name": "set_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21381310, "num_examples": 34602}, {"name": "validation", "num_bytes": 3077854, "num_examples": 5000}, {"name": "test", "num_bytes": 3025046, "num_examples": 5734}], "download_size": 8070116310, "dataset_size": 27484210}, {"config_name": "textvqa", "features": [{"name": "image_id", "dtype": "string"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "question_tokens", "sequence": "string"}, {"name": "image", "dtype": "image"}, {"name": "image_width", "dtype": "int32"}, {"name": "image_height", "dtype": "int32"}, {"name": "flickr_original_url", "dtype": "string"}, {"name": "flickr_300k_url", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "image_classes", "sequence": "string"}, {"name": "set_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22073350, "num_examples": 34602}, {"name": "validation", "num_bytes": 3177854, "num_examples": 5000}, {"name": "test", "num_bytes": 3139726, "num_examples": 5734}], "download_size": 8070116310, "dataset_size": 28390930}]} | 2024-01-18T11:19:08+00:00 |
42ea7b9b5daa33cc0509a6213c48da0ce8ae13aa | 00data00/data | [
"license:afl-3.0",
"region:us"
] | 2022-05-05T09:48:22+00:00 | {"license": "afl-3.0"} | 2022-05-05T09:48:22+00:00 |
|
45fcf91a8a00c86ad4b4c2034399dda55c5c9adc |
# Dataset Card for [Electricity Transformer Temperature](https://github.com/zhouhaoyi/ETDataset)
## 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:** [Electricity Transformer Dataset](https://github.com/zhouhaoyi/ETDataset)
- **Repository:** https://github.com/zhouhaoyi/ETDataset
- **Paper:** [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436)
- **Point of Contact:** [Haoyi Zhou](mailto:zhouhy@act.buaa.edu.cn)
### Dataset Summary
The electric power distribution problem is the distribution of electricity to different areas depending on its sequential usage. But predicting the future demand of a specific area is difficult, as it varies with weekdays, holidays, seasons, weather, temperatures, etc. However, no existing method can perform a long-term prediction based on super long-term real-world data with high precision. Any false predictions may damage the electrical transformer. So currently, without an efficient method to predict future electric usage, managers have to make decisions based on the empirical number, which is much higher than the real-world demands. It causes unnecessary waste of electric and equipment depreciation. On the other hand, the oil temperatures can reflect the condition of the Transformer. One of the most efficient strategies is to predict how the electrical transformers' oil temperature is safe and avoid unnecessary waste. As a result, to address this problem, the authors and Beijing Guowang Fuda Science & Technology Development Company have provided 2-years worth of data.
Specifically, the dataset combines short-term periodical patterns, long-term periodical patterns, long-term trends, and many irregular patterns. The dataset are obtained from 2 Electricity Transformers at 2 stations and come in an `1H` (hourly) or `15T` (15-minute) frequency containing 2 year * 365 days * 24 hours * (4 for 15T) times = 17,520 (70,080 for 15T) data points.
The target time series is the **O**il **T**emperature and the dataset comes with the following 6 covariates in the univariate setup:
* **H**igh **U**se**F**ul **L**oad
* **H**igh **U**se**L**ess **L**oad
* **M**iddle **U**se**F**ul **L**oad
* **M**iddle **U**se**L**ess **L**oad
* **L**ow **U**se**F**ul **L**oad
* **L**ow **U**se**L**ess **L**oad
### Dataset Usage
To load a particular variant of the dataset just specify its name e.g:
```python
load_dataset("ett", "m1", multivariate=False) # univariate 15-min frequency dataset from first transformer
```
or to specify a prediction length:
```python
load_dataset("ett", "h2", prediction_length=48) # multivariate dataset from second transformer with prediction length of 48 (hours)
```
### Supported Tasks and Leaderboards
The time series data is split into train/val/test set of 12/4/4 months respectively. Given the prediction length (default: 1 day (24 hours or 24*4 15T)) we create rolling windows of this size for the val/test sets.
#### `time-series-forecasting`
##### `univariate-time-series-forecasting`
The univariate time series forecasting tasks involves learning the future one dimensional `target` values of a time series in a dataset for some `prediction_length` time steps. The performance of the forecast models can then be validated via the ground truth in the `validation` split and tested via the `test` split. The covriates are stored in the `feat_dynamic_real` key of each time series.
##### `multivariate-time-series-forecasting`
The multivariate time series forecasting task involves learning the future vector of `target` values of a time series in a dataset for some `prediction_length` time steps. Similar to the univariate setting the performance of a multivariate model can be validated via the ground truth in the `validation` split and tested via the `test` split.
### Languages
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```python
{
'start': datetime.datetime(2012, 1, 1, 0, 0),
'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, ...],
'feat_static_cat': [0],
'feat_dynamic_real': [[0.3, 0.4], [0.1, 0.6], ...],
'item_id': 'OT'
}
```
### Data Fields
For the univariate regular time series each series has the following keys:
* `start`: a datetime of the first entry of each time series in the dataset
* `target`: an array[float32] of the actual target values
* `feat_static_cat`: an array[uint64] which contains a categorical identifier of each time series in the dataset
* `feat_dynamic_real`: optional array of covariate features
* `item_id`: a string identifier of each time series in a dataset for reference
For the multivariate time series the `target` is a vector of the multivariate dimension for each time point.
### Data Splits
The time series data is split into train/val/test set of 12/4/4 months respectively.
## Dataset Creation
### Curation Rationale
Develop time series methods that can perform a long-term prediction based on super long-term real-world data with high precision.
### 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
* [Haoyi Zhou](mailto:zhouhy@act.buaa.edu.cn)
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```tex
@inproceedings{haoyietal-informer-2021,
author = {Haoyi Zhou and
Shanghang Zhang and
Jieqi Peng and
Shuai Zhang and
Jianxin Li and
Hui Xiong and
Wancai Zhang},
title = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},
booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},
volume = {35},
number = {12},
pages = {11106--11115},
publisher = {{AAAI} Press},
year = {2021},
}
```
### Contributions
Thanks to [@kashif](https://github.com/kashif) for adding this dataset. | ett | [
"task_categories:time-series-forecasting",
"task_ids:univariate-time-series-forecasting",
"task_ids:multivariate-time-series-forecasting",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:cc-by-4.0",
"arxiv:2012.07436",
"region:us"
] | 2022-05-05T11:12:41+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": [], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["time-series-forecasting"], "task_ids": ["univariate-time-series-forecasting", "multivariate-time-series-forecasting"], "pretty_name": "Electricity Transformer Temperature", "dataset_info": [{"config_name": "h1", "features": [{"name": "start", "dtype": "timestamp[s]"}, {"name": "target", "sequence": "float32"}, {"name": "feat_static_cat", "sequence": "uint64"}, {"name": "feat_dynamic_real", "sequence": {"sequence": "float32"}}, {"name": "item_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 241978, "num_examples": 1}, {"name": "test", "num_bytes": 77508960, "num_examples": 240}, {"name": "validation", "num_bytes": 33916080, "num_examples": 120}], "download_size": 2589657, "dataset_size": 111667018}, {"config_name": "h2", "features": [{"name": "start", "dtype": "timestamp[s]"}, {"name": "target", "sequence": "float32"}, {"name": "feat_static_cat", "sequence": "uint64"}, {"name": "feat_dynamic_real", "sequence": {"sequence": "float32"}}, {"name": "item_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 241978, "num_examples": 1}, {"name": "test", "num_bytes": 77508960, "num_examples": 240}, {"name": "validation", "num_bytes": 33916080, "num_examples": 120}], "download_size": 2417960, "dataset_size": 111667018}, {"config_name": "m1", "features": [{"name": "start", "dtype": "timestamp[s]"}, {"name": "target", "sequence": "float32"}, {"name": "feat_static_cat", "sequence": "uint64"}, {"name": "feat_dynamic_real", "sequence": {"sequence": "float32"}}, {"name": "item_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 967738, "num_examples": 1}, {"name": "test", "num_bytes": 1239008640, "num_examples": 960}, {"name": "validation", "num_bytes": 542089920, "num_examples": 480}], "download_size": 10360719, "dataset_size": 1782066298}, {"config_name": "m2", "features": [{"name": "start", "dtype": "timestamp[s]"}, {"name": "target", "sequence": "float32"}, {"name": "feat_static_cat", "sequence": "uint64"}, {"name": "feat_dynamic_real", "sequence": {"sequence": "float32"}}, {"name": "item_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 967738, "num_examples": 1}, {"name": "test", "num_bytes": 1239008640, "num_examples": 960}, {"name": "validation", "num_bytes": 542089920, "num_examples": 480}], "download_size": 9677236, "dataset_size": 1782066298}]} | 2024-01-18T11:19:09+00:00 |
7d5a48f50f02568d74fb4d0ca4c333684addc26d | # hotpotExtendedAno-LM
Version of hotpotExtended-Annotated formatted for Language Modelling. | ghomasHudson/hotpotExtendedAnoLM | [
"region:us"
] | 2022-05-05T13:07:24+00:00 | {} | 2022-05-05T13:09:26+00:00 |
7d23ca7e27a34288f7083619602a7a902938ead9 |
# Doctor Who Dialogues
This dataset contains all the script lines of Eleventh Doctor from Doctor Who which is a popular TV series. It can be processed and used for chatbots or relevant stuff. | truthisneverlinear/eleventh-doctor-scripts | [
"language:en",
"NLP",
"conservation",
"dialogue",
"region:us"
] | 2022-05-05T13:34:18+00:00 | {"language": "en", "tags": ["NLP", "conservation", "dialogue"]} | 2022-05-05T13:39:18+00:00 |
bd758693b05d8405157aa662564a93edc53c6be7 | boli-ai-admin/vishal | [
"license:apache-2.0",
"region:us"
] | 2022-05-05T13:59:54+00:00 | {"license": "apache-2.0"} | 2022-05-05T13:59:54+00:00 |
|
3997b23648a3961e1e566d976ac9310aa2ac5e20 |
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI email](mailto:idx_aqua@sil.org)
- **Source Data:** [Bloom Library](https://bloomlibrary.org/)
 
## Dataset Summary
**Bloom** is free, open-source software and an associated website [Bloom Library](https://bloomlibrary.org/), app, and services developed by [SIL International](https://www.sil.org/). Bloom’s primary goal is to equip non-dominant language communities and their members to create the literature they want for their community and children. Bloom also serves organizations that help such communities develop literature and education or other aspects of community development.
This version of the Bloom Library data is developed specifically for the visual story telling (or VIST) task. It includes data from 364 languages across 31 language families. There is a mean of 32 stories and median of 2 stories per language.
**Note**: If you speak one of these languages and can help provide feedback or corrections, please let us know!
**Note**: Although this data was used in the training of the [BLOOM model](https://huggingface.co/bigscience/bloom), this dataset only represents a small portion of the data used to train that model. Data from "Bloom Library" was combined with a large number of other datasets to train that model. "Bloom Library" is a project that existed prior to the BLOOM model, and is something separate. All that to say... We were using the "Bloom" name before it was cool. 😉
## Languages
Of the 500+ languages listed at BloomLibrary.org, there are 363 languages available in this dataset. Here are the corresponding ISO 639-3 codes:
aaa, abc, ada, adq, aeu, afr, agq, ags, ahk, aia, ajz, aka, ame, amh, amp, amu, ann, aph, awa, awb, azn, azo, bag, bam, baw, bax, bbk, bcc, bce, bec, bef, ben, bfd, bfm, bfn, bgf, bho, bhs, bis, bjn, bjr, bkc, bkh, bkm, bkx, bob, bod, boz, bqm, bra, brb, bri, brv, bss, bud, buo, bwt, bwx, bxa, bya, bze, bzi, cak, cbr, ceb, cgc, chd, chp, cim, clo, cmn, cmo, csw, cuh, cuv, dag, ddg, ded, deu, dig, dje, dmg, dnw, dtp, dtr, dty, dug, eee, ekm, enb, enc, eng, ewo, fas, fil, fli, fon, fra, fub, fuh, gal, gbj, gou, gsw, guc, guj, guz, gwc, hao, hat, hau, hbb, hig, hil, hin, hla, hna, hre, hro, idt, ilo, ind, ino, isu, ita, jgo, jmx, jpn, jra, kak, kam, kan, kau, kbq, kbx, kby, kek, ken, khb, khm, kik, kin, kir, kjb, kmg, kmr, kms, kmu, kor, kqr, krr, ksw, kur, kvt, kwd, kwu, kwx, kxp, kyq, laj, lan, lao, lbr, lfa, lgg, lgr, lhm, lhu, lkb, llg, lmp, lns, loh, lsi, lts, lug, luy, lwl, mai, mal, mam, mar, mdr, mfh, mfj, mgg, mgm, mgo, mgq, mhx, miy, mkz, mle, mlk, mlw, mmu, mne, mnf, mnw, mot, mqj, mrn, mry, msb, muv, mve, mxu, mya, myk, myx, mzm, nas, nco, nep, new, nge, ngn, nhx, njy, nla, nld, nlv, nod, nsk, nsn, nso, nst, nuj, nwe, nwi, nxa, nxl, nya, nyo, nyu, nza, odk, oji, oki, omw, ori, ozm, pae, pag, pan, pbt, pce, pcg, pdu, pea, pex, pis, pkb, pmf, pnz, por, psp, pwg, qub, quc, quf, quz, qve, qvh, qvm, qvo, qxh, rel, rnl, ron, roo, rue, rug, rus, san, saq, sat, sdk, sea, sgd, shn, sml, snk, snl, som, sot, sox, spa, sps, ssn, stk, swa, swh, sxb, syw, taj, tam, tbj, tdb, tdg, tdt, teo, tet, tgk, tha, the, thk, thl, thy, tio, tkd, tnl, tnn, tnp, tnt, tod, tom, tpi, tpl, tpu, tsb, tsn, tso, tuv, tuz, tvs, udg, unr, urd, uzb, ven, vie, vif, war, wbm, wbr, wms, wni, wnk, wtk, xho, xkg, xmd, xmg, xmm, xog, xty, yas, yav, ybb, ybh, ybi, ydd, yea, yet, yid, yin, ymp, zaw, zho, zlm, zuh, zul
## Dataset Statistics
Some of the languages included in the dataset just include 1 or a couple of "stories." For those with higher numbers of available stories we include the following numbers of stories:
| ISO639-3 Code | Stories | Image-Caption Pairs |
|:-----------|----------:|----------------------:|
| ahk | 55 | 493 |
| awa | 163 | 1200 |
| ben | 220 | 1938 |
| bho | 172 | 1163 |
| bis | 21 | 183 |
| brb | 22 | 330 |
| bzi | 66 | 497 |
| cak | 50 | 694 |
| ceb | 394 | 2806 |
| cgc | 182 | 1473 |
| deu | 22 | 250 |
| dty | 172 | 1310 |
| eng | 2187 | 24338 |
| fas | 128 | 620 |
| fil | 34 | 366 |
| fra | 315 | 4350 |
| hat | 224 | 1881 |
| hau | 229 | 1594 |
| ind | 232 | 1866 |
| jra | 56 | 575 |
| kak | 195 | 1416 |
| kek | 21 | 419 |
| khb | 31 | 167 |
| khm | 26 | 246 |
| kir | 278 | 2866 |
| kjb | 63 | 584 |
| kor | 129 | 2732 |
| krr | 29 | 362 |
| lsi | 22 | 173 |
| mai | 177 | 1186 |
| mam | 118 | 1058 |
| mhx | 51 | 544 |
| myk | 22 | 214 |
| nep | 194 | 1464 |
| new | 177 | 1225 |
| pbt | 203 | 979 |
| por | 148 | 2939 |
| quc | 99 | 817 |
| rus | 271 | 2977 |
| snk | 21 | 210 |
| spa | 444 | 5201 |
| swh | 34 | 387 |
| tdg | 31 | 231 |
| tha | 275 | 2929 |
| thl | 185 | 1464 |
| tpi | 137 | 1528 |
| tpu | 28 | 513 |
| zho | 42 | 339 |
## Dataset Structure
### Data Instances
The examples look like this for Hindi:
```
from datasets import load_dataset
# Specify the language code.
dataset = load_dataset("sil-ai/bloom-vist", 'hin')
# An individual samples consists of stories in the specified language code.
# To see a story:
print(dataset['train'][0]['story'])
```
This would produce an output:
```
{'image_id': ['4e9bdde5-996d-4a98-ac1c-d80fb6349314',
'614e4d51-bbdb-4538-98d3-f603c12dccd0',
'970d60bf-2acb-44ac-8ffb-5aa3f7989630',
'd4ad1199-863e-4929-a377-93276fe5caa8',
'0d9ad694-995a-433d-af4e-6f40ddfa208a',
'811176eb-c9f3-4226-8af5-e6c4e524c494',
'83180da7-4ba8-4104-a0d9-49aa2ef48f7a'],
'image_url': ['https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_03_Image_00011.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_04_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_05_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_06_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_07_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_07_Image_00011.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_09_Image_0001.png'],
'story_index': [0, 1, 2, 3, 4, 5, 6],
'story_id': ['cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6'],
'text': ['साबू ने एक कंकड़ को ठोकर मारी। कंकड़ लुढ़कता हुआ एक पेड़ के पास पहुँचा। पेड़ के तने पर मुलायम बाल थे। साबू ने छुए और ऊपर देखा, ऊपर, ऊपर और उससे भी ऊपर...दो आँखें नीचे देख रही थीं।',
'“हेलो, तुम कौन हो?” साबू को बड़ा अचम्भा हुआ।“हेलो, मैं जिराफ़ हूँ। मेरा नाम है जोजो। \xa0मैं तुम्हारे साथ खेल सकता हूँ। मेरी पीठ पर चढ़ जाओ, मैं तुम्हें घुमा के लाता हूँ।”',
'साबू जोजो की पीठ पर चढ़ गया और वे सड़क पर चल निकले। फिर पहाड़ी पर और शहर के बीचों बीच।\nसाबू खुशी से चिल्लाया, “जोजो दाएँ मुड़ो,\n बाएँ मुड़ो और फिर दाएँ।” अब वे उसकी दोस्त मुन्नी के घर पहुँच गये।',
'आज मुन्नी का जन्मदिन था। साबू को जोजो पर सवारी करते देख बच्चों ने ताली बजायी।\xa0\n जोजो ने गुब्बारे लटकाने में आन्टी की मदद करी क्योंकि वह इतना... लम्बा था।\xa0\n कितना आसान था!',
'जोजो ने सब बच्चों को सवारी कराई।\n उनके साथ बॉल भी खेली। बड़े मज़े की पार्टी थी।सब ने गाया, “हैप्पी बर्थ डे टु यू ।”\n आन्टी ने मेज़ पर समोसे, गुलाब जामुन और आइसक्रीम सजाई।',
'जोजो को आइसक्रीम बहुत पसन्द आई। अंकल उसके लिये एक बाल्टी भर के आइसक्रीम लाये। जोजो ने पूरी बाल्टी ख़त्म कर दी। \xa0अब घर जाने का समय हो गया।\n\nसब ने कहा, “बाय बाय जोजो, बाय बाय साबू।” साबू और जोजो घर लौटे।',
'']}
```
### Data Fields
The metadata fields below are available. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).
- **id**: id of the sample
- **title**: title of the book, e.g. "Going to Buy a Book".
- **license**: specific license used, e.g. "cc-by-sa" for "Creative Commons, by attribution, share-alike".
- **album_id**: an ID value corresponding to the set of images corresponding to the given story
- **story**: the sequenced story data including lists of image IDs, image URLs, and corresponding text
### Data Splits
Currently all languages include a train split only. In the future, we will be creating manual splits of the data.
## Changelog
- **6 December 2022** - dataset is made public | sil-ai/bloom-vist | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:afr",
"language:af",
"language:aaa",
"language:abc",
"language:ada",
"language:adq",
"language:aeu",
"language:agq",
"language:ags",
"language:ahk",
"language:aia",
"language:ajz",
"language:aka",
"language:ak",
"language:ame",
"language:amh",
"language:am",
"language:amp",
"language:amu",
"language:ann",
"language:aph",
"language:awa",
"language:awb",
"language:azn",
"language:azo",
"language:bag",
"language:bam",
"language:bm",
"language:baw",
"language:bax",
"language:bbk",
"language:bcc",
"language:bce",
"language:bec",
"language:bef",
"language:ben",
"language:bn",
"language:bfd",
"language:bfm",
"language:bfn",
"language:bgf",
"language:bho",
"language:bhs",
"language:bis",
"language:bi",
"language:bjn",
"language:bjr",
"language:bkc",
"language:bkh",
"language:bkm",
"language:bkx",
"language:bob",
"language:bod",
"language:bo",
"language:boz",
"language:bqm",
"language:bra",
"language:brb",
"language:bri",
"language:brv",
"language:bss",
"language:bud",
"language:buo",
"language:bwt",
"language:bwx",
"language:bxa",
"language:bya",
"language:bze",
"language:bzi",
"language:cak",
"language:cbr",
"language:ceb",
"language:cgc",
"language:chd",
"language:chp",
"language:cim",
"language:clo",
"language:cmn",
"language:zh",
"language:cmo",
"language:csw",
"language:cuh",
"language:cuv",
"language:dag",
"language:ddg",
"language:ded",
"language:deu",
"language:de",
"language:dig",
"language:dje",
"language:dmg",
"language:dnw",
"language:dtp",
"language:dtr",
"language:dty",
"language:dug",
"language:eee",
"language:ekm",
"language:enb",
"language:enc",
"language:eng",
"language:en",
"language:ewo",
"language:fas",
"language:fa",
"language:fil",
"language:fli",
"language:fon",
"language:fra",
"language:fr",
"language:fub",
"language:fuh",
"language:gal",
"language:gbj",
"language:gou",
"language:gsw",
"language:guc",
"language:guj",
"language:gu",
"language:guz",
"language:gwc",
"language:hao",
"language:hat",
"language:ht",
"language:hau",
"language:ha",
"language:hbb",
"language:hig",
"language:hil",
"language:hin",
"language:hi",
"language:hla",
"language:hna",
"language:hre",
"language:hro",
"language:idt",
"language:ilo",
"language:ind",
"language:id",
"language:ino",
"language:isu",
"language:ita",
"language:it",
"language:jgo",
"language:jmx",
"language:jpn",
"language:ja",
"language:jra",
"language:kak",
"language:kam",
"language:kan",
"language:kn",
"language:kau",
"language:kr",
"language:kbq",
"language:kbx",
"language:kby",
"language:kek",
"language:ken",
"language:khb",
"language:khm",
"language:km",
"language:kik",
"language:ki",
"language:kin",
"language:rw",
"language:kir",
"language:ky",
"language:kjb",
"language:kmg",
"language:kmr",
"language:ku",
"language:kms",
"language:kmu",
"language:kor",
"language:ko",
"language:kqr",
"language:krr",
"language:ksw",
"language:kur",
"language:kvt",
"language:kwd",
"language:kwu",
"language:kwx",
"language:kxp",
"language:kyq",
"language:laj",
"language:lan",
"language:lao",
"language:lo",
"language:lbr",
"language:lfa",
"language:lgg",
"language:lgr",
"language:lhm",
"language:lhu",
"language:lkb",
"language:llg",
"language:lmp",
"language:lns",
"language:loh",
"language:lsi",
"language:lts",
"language:lug",
"language:lg",
"language:luy",
"language:lwl",
"language:mai",
"language:mal",
"language:ml",
"language:mam",
"language:mar",
"language:mr",
"language:mdr",
"language:mfh",
"language:mfj",
"language:mgg",
"language:mgm",
"language:mgo",
"language:mgq",
"language:mhx",
"language:miy",
"language:mkz",
"language:mle",
"language:mlk",
"language:mlw",
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"language:mne",
"language:mnf",
"language:mnw",
"language:mot",
"language:mqj",
"language:mrn",
"language:mry",
"language:msb",
"language:muv",
"language:mve",
"language:mxu",
"language:mya",
"language:my",
"language:myk",
"language:myx",
"language:mzm",
"language:nas",
"language:nco",
"language:nep",
"language:ne",
"language:new",
"language:nge",
"language:ngn",
"language:nhx",
"language:njy",
"language:nla",
"language:nld",
"language:nl",
"language:nlv",
"language:nod",
"language:nsk",
"language:nsn",
"language:nso",
"language:nst",
"language:nuj",
"language:nwe",
"language:nwi",
"language:nxa",
"language:nxl",
"language:nya",
"language:ny",
"language:nyo",
"language:nyu",
"language:nza",
"language:odk",
"language:oji",
"language:oj",
"language:oki",
"language:omw",
"language:ori",
"language:or",
"language:ozm",
"language:pae",
"language:pag",
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"language:pa",
"language:pbt",
"language:pce",
"language:pcg",
"language:pdu",
"language:pea",
"language:pex",
"language:pis",
"language:pkb",
"language:pmf",
"language:pnz",
"language:por",
"language:pt",
"language:psp",
"language:pwg",
"language:qaa",
"language:qub",
"language:quc",
"language:quf",
"language:quz",
"language:qve",
"language:qvh",
"language:qvm",
"language:qvo",
"language:qxh",
"language:rel",
"language:rnl",
"language:ron",
"language:ro",
"language:roo",
"language:rue",
"language:rug",
"language:rus",
"language:ru",
"language:san",
"language:sa",
"language:saq",
"language:sat",
"language:sdk",
"language:sea",
"language:sgd",
"language:shn",
"language:sml",
"language:snk",
"language:snl",
"language:som",
"language:so",
"language:sot",
"language:st",
"language:sox",
"language:spa",
"language:es",
"language:sps",
"language:ssn",
"language:stk",
"language:swa",
"language:sw",
"language:swh",
"language:sxb",
"language:syw",
"language:taj",
"language:tam",
"language:ta",
"language:tbj",
"language:tdb",
"language:tdg",
"language:tdt",
"language:teo",
"language:tet",
"language:tgk",
"language:tg",
"language:tha",
"language:th",
"language:the",
"language:thk",
"language:thl",
"language:thy",
"language:tio",
"language:tkd",
"language:tnl",
"language:tnn",
"language:tnp",
"language:tnt",
"language:tod",
"language:tom",
"language:tpi",
"language:tpl",
"language:tpu",
"language:tsb",
"language:tsn",
"language:tn",
"language:tso",
"language:ts",
"language:tuv",
"language:tuz",
"language:tvs",
"language:udg",
"language:unr",
"language:urd",
"language:ur",
"language:uzb",
"language:uz",
"language:ven",
"language:ve",
"language:vie",
"language:vi",
"language:vif",
"language:war",
"language:wbm",
"language:wbr",
"language:wms",
"language:wni",
"language:wnk",
"language:wtk",
"language:xho",
"language:xh",
"language:xkg",
"language:xmd",
"language:xmg",
"language:xmm",
"language:xog",
"language:xty",
"language:yas",
"language:yav",
"language:ybb",
"language:ybh",
"language:ybi",
"language:ydd",
"language:yea",
"language:yet",
"language:yid",
"language:yi",
"language:yin",
"language:ymp",
"language:zaw",
"language:zho",
"language:zlm",
"language:zuh",
"language:zul",
"language:zu",
"license:cc-by-4.0",
"license:cc-by-nc-4.0",
"license:cc-by-nd-4.0",
"license:cc-by-sa-4.0",
"license:cc-by-nc-nd-4.0",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-05-05T15:25:54+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["afr", "af", "aaa", "abc", "ada", "adq", "aeu", "agq", "ags", "ahk", "aia", "ajz", "aka", "ak", "ame", "amh", "am", "amp", "amu", "ann", "aph", "awa", "awb", "azn", "azo", "bag", "bam", "bm", "baw", "bax", "bbk", "bcc", "bce", "bec", "bef", "ben", "bn", "bfd", "bfm", "bfn", "bgf", "bho", "bhs", "bis", "bi", "bjn", "bjr", "bkc", "bkh", "bkm", "bkx", "bob", "bod", "bo", "boz", "bqm", "bra", "brb", "bri", "brv", "bss", "bud", "buo", "bwt", "bwx", "bxa", "bya", "bze", "bzi", "cak", "cbr", "ceb", "cgc", "chd", "chp", "cim", "clo", "cmn", "zh", "cmo", "csw", "cuh", "cuv", "dag", "ddg", "ded", "deu", "de", "dig", "dje", "dmg", "dnw", "dtp", "dtr", "dty", "dug", "eee", "ekm", "enb", "enc", "eng", "en", "ewo", "fas", "fa", "fil", "fli", "fon", "fra", "fr", "fub", "fuh", "gal", "gbj", "gou", "gsw", "guc", "guj", "gu", "guz", "gwc", "hao", "hat", "ht", "hau", "ha", "hbb", "hig", "hil", "hin", "hi", "hla", "hna", "hre", "hro", "idt", "ilo", "ind", "id", "ino", "isu", "ita", "it", "jgo", "jmx", "jpn", "ja", "jra", "kak", "kam", "kan", "kn", "kau", "kr", "kbq", "kbx", "kby", "kek", "ken", "khb", "khm", "km", "kik", "ki", "kin", "rw", "kir", "ky", "kjb", "kmg", "kmr", "ku", "kms", "kmu", "kor", "ko", "kqr", "krr", "ksw", "kur", "ku", "kvt", "kwd", "kwu", "kwx", "kxp", "kyq", "laj", "lan", "lao", "lo", "lbr", "lfa", "lgg", "lgr", "lhm", "lhu", "lkb", "llg", "lmp", "lns", "loh", "lsi", "lts", "lug", "lg", "luy", "lwl", "mai", "mal", "ml", "mam", "mar", "mr", "mdr", "mfh", "mfj", "mgg", "mgm", "mgo", "mgq", "mhx", "miy", "mkz", "mle", "mlk", "mlw", "mmu", "mne", "mnf", "mnw", "mot", "mqj", "mrn", "mry", "msb", "muv", "mve", "mxu", "mya", "my", "myk", "myx", "mzm", "nas", "nco", "nep", "ne", "new", "nge", "ngn", "nhx", "njy", "nla", "nld", "nl", "nlv", "nod", "nsk", "nsn", "nso", "nst", "nuj", "nwe", "nwi", "nxa", "nxl", "nya", "ny", "nyo", "nyu", "nza", "odk", "oji", "oj", "oki", "omw", "ori", "or", "ozm", "pae", "pag", "pan", "pa", "pbt", "pce", "pcg", "pdu", "pea", "pex", "pis", "pkb", "pmf", "pnz", "por", "pt", "psp", "pwg", "qaa", "qub", "quc", "quf", "quz", "qve", "qvh", "qvm", "qvo", "qxh", "rel", "rnl", "ron", "ro", "roo", "rue", "rug", "rus", "ru", "san", "sa", "saq", "sat", "sdk", "sea", "sgd", "shn", "sml", "snk", "snl", "som", "so", "sot", "st", "sox", "spa", "es", "sps", "ssn", "stk", "swa", "sw", "swh", "sxb", "syw", "taj", "tam", "ta", "tbj", "tdb", "tdg", "tdt", "teo", "tet", "tgk", "tg", "tha", "th", "the", "thk", "thl", "thy", "tio", "tkd", "tnl", "tnn", "tnp", "tnt", "tod", "tom", "tpi", "tpl", "tpu", "tsb", "tsn", "tn", "tso", "ts", "tuv", "tuz", "tvs", "udg", "unr", "urd", "ur", "uzb", "uz", "ven", "ve", "vie", "vi", "vif", "war", "wbm", "wbr", "wms", "wni", "wnk", "wtk", "xho", "xh", "xkg", "xmd", "xmg", "xmm", "xog", "xty", "yas", "yav", "ybb", "ybh", "ybi", "ydd", "yea", "yet", "yid", "yi", "yin", "ymp", "zaw", "zho", "zh", "zlm", "zuh", "zul", "zu"], "license": ["cc-by-4.0", "cc-by-nc-4.0", "cc-by-nd-4.0", "cc-by-sa-4.0", "cc-by-nc-nd-4.0", "cc-by-nc-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "pretty_name": "BloomVIST", "extra_gated_prompt": "One more step before getting this dataset. This dataset is open access and available only for non-commercial use (except for portions of the dataset labeled with a `cc-by-sa` license). A \"license\" field paired with each of the dataset entries/samples specifies the Creative Commons license for that entry/sample.\n\nThese [Creative Commons licenses](https://creativecommons.org/about/cclicenses/) specify that: \n\n1. You cannot use the dataset for or directed toward commercial advantage or monetary compensation (except for those portions of the dataset labeled specifically with a `cc-by-sa` license. If you would like to ask about commercial uses of this dataset, please [email us](mailto:sj@derivation.co).\n2. Any public, non-commercial use of the data must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. \n3. For those portions of the dataset marked with an ND license, you cannot remix, transform, or build upon the material, and you may not distribute modified material. \n\nIn addition to the above implied by Creative Commons and when clicking \"Access Repository\" below, you agree: \n\n1. Not to use the dataset for any use intended to or which has the effect of harming or enabling discrimination against individuals or groups based on legally protected characteristics or categories, including but not limited to discrimination against Indigenous People as outlined in Articles 2; 13-16; and 31 of the United Nations Declaration on the Rights of Indigenous People, 13 September 2007 and as subsequently amended and revised.\n2. That your *contact information* (email address and username) can be shared with the model authors as well.\n ", "extra_gated_fields": {"I have read the License and agree with its terms": "checkbox"}} | 2022-12-06T19:37:53+00:00 |
aa413c82b227dd25308df571e8b9d26e034cf2f7 | # Gcode (Geometric code)
## Details
**Usage:** 3D printing <br>
**Source:** Printables.com <br>
**Slicer:** Prusa <br>
**Category:** Art & Design <br>
**Subcategory:** Sculptures <br>
**Models:** 400 <br>
**Sliced files:** 740 (some models have many) <br>
**Data format:** txt <br>
**Train-test split:** 90/10 <br>
**Size:** 11GB
| ablam/gcode | [
"region:us"
] | 2022-05-05T16:27:08+00:00 | {} | 2022-05-05T18:14:30+00:00 |
cdb6315d2d523ee509cb915eb37a2ac1f807da2f | # Dataset for the first <a href="https://ui.adsabs.harvard.edu/WIESP/" style="color:blue">Workshop on Information Extraction from Scientific Publications (WIESP/2022)</a>.
## Dataset Description
Datasets with text fragments from astrophysics papers, provided by the [NASA Astrophysical Data System](https://ui.adsabs.harvard.edu/) with manually tagged astronomical facilities and other entities of interest (e.g., celestial objects).
Datasets are in JSON Lines format (each line is a json dictionary).
The datasets are formatted similarly to the CONLL2003 format. Each token is associated with an NER tag. The tags follow the "B-" and "I-" convention from the [IOB2 syntax]("https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)")
Each entry consists of a dictionary with the following keys:
- `"unique_id"`: a unique identifier for this data sample. Must be included in the predictions.
- `"tokens"`: the list of tokens (strings) that form the text of this sample. Must be included in the predictions.
- `"ner_tags"`: the list of NER tags (in IOB2 format)
The following keys are not strictly needed by the participants:
- `"ner_ids"`: the pre-computed list of ids corresponding ner_tags, as given by the dictionary in ner_tags.json
- `"label_studio_id"`, `"section"`, `"bibcode"`: references for internal NASA/ADS use.
## Instructions for Workshop participants:
How to load the data using the Huggingface library:
```python
from datasets import load_dataset
dataset = load_dataset("adsabs/WIESP2022-NER")
```
How to load the data if you cloned the repository locally:
(assuming `./WIESP2022-NER-DEV.jsonl` is in the current directory, change as needed)
- python (as list of dictionaries):
```python
import json
with open("./WIESP2022-NER-DEV.jsonl", 'r') as f:
wiesp_dev_json = [json.loads(l) for l in list(f)]
```
- into Huggingface (as a Huggingface Dataset):
```python
from datasets import Dataset
wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.jsonl")
```
How to compute your scores on the training data:
1. format your predictions as a list of dictionaries, each with the same `"unique_id"` and `"tokens"` keys from the dataset, as well as the list of predicted NER tags under the `"pred_ner_tags"` key (see `WIESP2022-NER-DEV-sample-predictions.jsonl` for an example).
2. pass the references and predictions datasets to the `compute_MCC()` and `compute_seqeval()` functions (from the `.py` files with the same names).
Requirement to run the scoring scripts:
[NumPy](https://numpy.org/install/)
[scikit-learn](https://scikit-learn.org/stable/install.html)
[seqeval](https://github.com/chakki-works/seqeval#installation)
To get scores on the validation data, zip your predictions file (a single `.jsonl' file formatted following the same instructions as above) and upload the `.zip` file to the [Codalabs](https://codalab.lisn.upsaclay.fr/competitions/5062) competition.
## File list
```
├── WIESP2022-NER-TRAINING.jsonl : 1753 samples for training.
├── WIESP2022-NER-DEV.jsonl : 20 samples for development.
├── WIESP2022-NER-DEV-sample-predictions.jsonl : an example file with properly formatted predictions on the development data.
├── WIESP2022-NER-VALIDATION-NO-LABELS.jsonl : 1366 samples for validation without the NER labels. Used for the WIESP2022 workshop.
├── WIESP2022-NER-VALIDATION.jsonl : 1366 samples for validation
├── WIESP2022-NER-TESTING-NO-LABELS.jsonl : 2505 samples for testing without the NER labels. Used for the WIESP2022 workshop.
├── WIESP2022-NER-TESTING.jsonl : 2505 samples for testing
├── README.MD : this file.
├── tag_definitions.md : short descriptions and examples of the tags used in the task.
└── scoring-scripts/ : scripts used to evaluate submissions.
├── compute_MCC.py : computes the Matthews correlation coefficient between two datasets.
└── compute_seqeval.py : computes the seqeval scores (precision, recall, f1, overall and for each class) between two datasets.
```
## Cite as
[Overview of the First Shared Task on Detecting Entities in the Astrophysics Literature (DEAL)](https://aclanthology.org/2022.wiesp-1.1) (Grezes et al., WIESP 2022)
```python
@inproceedings{grezes-etal-2022-overview,
title = "Overview of the First Shared Task on Detecting Entities in the Astrophysics Literature ({DEAL})",
author = "Grezes, Felix and
Blanco-Cuaresma, Sergi and
Allen, Thomas and
Ghosal, Tirthankar",
booktitle = "Proceedings of the first Workshop on Information Extraction from Scientific Publications",
month = "nov",
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wiesp-1.1",
pages = "1--7",
abstract = "In this article, we describe the overview of our shared task: Detecting Entities in the Astrophysics Literature (DEAL). The DEAL shared task was part of the Workshop on Information Extraction from Scientific Publications (WIESP) in AACL-IJCNLP 2022. Information extraction from scientific publications is critical in several downstream tasks such as identification of critical entities, article summarization, citation classification, etc. The motivation of this shared task was to develop a community-wide effort for entity extraction from astrophysics literature. Automated entity extraction would help to build knowledge bases, high-quality meta-data for indexing and search, and several other use-cases of interests. Thirty-three teams registered for DEAL, twelve of them participated in the system runs, and finally four teams submitted their system descriptions. We analyze their system and performance and finally discuss the findings of DEAL.",
}
``` | adsabs/WIESP2022-NER | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-05-05T17:31:34+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "WIESP2022-NER"} | 2023-05-17T18:42:32+00:00 |
6ef57a87aea2fddaa26e86f7f3eda48ba3171a8b | nateraw/gradio-guides-files | [
"license:mit",
"region:us"
] | 2022-05-05T20:06:47+00:00 | {"license": "mit"} | 2022-05-05T20:07:09+00:00 |
|
b7b7a73dbc74b681b384048716232b243ad25a99 |
# Dataset Card for ImageCoDe
To get started quickly, load descriptions via:
```
from datasets import load_dataset
examples = load_dataset('BennoKrojer/ImageCoDe')
```
And download `image_sets.zip` for all images sets (each directory consisting of 10 images).
## Dataset Description
- **Homepage & Leaderboard:** https://mcgill-nlp.github.io/imagecode/
- **Repository:** https://github.com/McGill-NLP/imagecode
- **Paper:** https://arxiv.org/abs/2203.15867
- **Point of Contact:** benno DOT krojer ÄT gmail DOT com
### Dataset Summary
We introduce ImageCoDe, a vision-and-language benchmark that requires contextual language understanding in the form of pragmatics, temporality, long descriptions and visual nuances. The task: Given a detailed description, retrieve the target image among 10 minimally contrastive images. ImageCoDe contains 21K descriptions and 94K images. THe images are primarily frames based on video datasets.
## Dataset Structure
### Data Instances
An instance contains a description, the corresponding image set name, and the target index:
```
{"image_set": "video-storytelling-videowedding_de8dLXvgV-I-shot6_0",
"image_index": "8",
"description": "The flowers the woman in the teal strapless dress is carrying are completely obscured by the man in the black shirt's head. "}
```
### Data Splits
| Dataset Split | Number of Descriptions in Split |
| ------------- |----------------------------- |
| Train | 16,594 |
| Validation | 2,302 |
| Test | 2,306 |
## Dataset Creation
### Curation Rationale
The main goal of ImageCoDe is to highlight weaknesses of recent Vision-and-Language models regarding complex language and fine-grained visual representations. In addition, we found that the dataset offers plenty of pragmatic examples and is therefore suitable for studying pragmatics. | BennoKrojer/ImageCoDe | [
"license:afl-3.0",
"arxiv:2203.15867",
"region:us"
] | 2022-05-05T20:50:13+00:00 | {"license": "afl-3.0"} | 2022-05-13T20:26:08+00:00 |
3adb85380e4645c1fcf914a21ba4013410c0fa2b | # test-imagefolder-metadata | nateraw/test-imagefolder-metadata | [
"region:us"
] | 2022-05-05T23:18:59+00:00 | {} | 2022-05-06T00:51:57+00:00 |
e1623289640e76fe2209e753a1b78a2200edc34e |
# Vietnamese Text-To-Speech dataset (VietTTS-v1.1)
🔔🔔🔔 visit https://github.com/NTT123/vietTTS for a vietnamese TTS library (included pretrained models). 🔔🔔🔔
The text is from a collection of novels and short stories from the author "Vu Trong Phung." The text is in public domain.
The audio is generated by Google Text-to-Speech offline engine on Android. The audio is NOT for commercial use.
Dataset size: `5.4G`.
Total audio duration: `35.9 hours`.
### Text-audio samples
- Sample 1:
+ Audio: [file1](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/000000.wav)
+ Text: `"Ai" đây tức là một kẻ ăn mày vậy. Anh ta chưa kịp quay đi thì đã thấy mấy con chó vàng chạy xồng xộc ra cứ nhảy xổ vào chân anh.`
- Sample 2:
+ Audio: [file2](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/022878.wav)
+ Text: `Ừ, thế mày đã nuôi được bố mẹ mày bữa nào chưa, hay xưa nay vẫn báo hại cơm cha áo mẹ mãi? Mấy hôm thấy ông đơ mặt không thèm nói, mày lại làm già à?`
### Download
Get the dataset from here: [link](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/viet-tts.tar.gz).
Or, run the following commands:
```
wget https://huggingface.co/datasets/ntt123/viet-tts-dataset/resolve/main/viet-tts.tar.gz -O viet-tts.tar.gz
mkdir -p dataset
tar -C dataset -xzf viet-tts.tar.gz
```
`dataset` directory structure:
```
dataset
├── collections.txt
├── meta_data.tsv
└── wav
├── 000000.wav
├── 000001.wav
├── 000002.wav
├── 000003.wav
...
```
### Statistics
- Number of clips: 22884 clips.
- Shortest audio clip: 0.46 seconds.
- Median clip duration: 5.46 seconds.
- Mean clip duration: 5.65 seconds.
- Longest audio clip: 15.4 seconds.
### Vũ Trọng Phụng's collections
- Bệnh Lao Chữa Bằng Mồm Hay Là ... Thầy Lang Bất Hủ, 1934?
- Cạm Bẫy Người, 1933.
- Cơm Thầy Cơm Cô, 1936.
- Đời Là Một Cuộc Chiến Đấu,1939.
- Dứt Tình, 1934.
- Giông Tố, 1936.
- Gương Tống Tiền, N/A.
- Hồ Sê Líu, Hồ Líu Sê Sàng, 1936.
- Kỹ Nghệ Lấy Tây, 1934.
- Làm Đĩ, 1936.
- Lấy Nhau Vì Tình, 1937.
- Lấy Vợ Xấu, 1937.
- Lòng Tự Ái, 1937.
- Máu Mê, 1937.
- Một Cái Chết, 1931.
- Một Con Chó Hay Chim Chuột, 1937.
- Một Đồng Bạc, 1939.
- Người Có Quyền, 1937.
- Sao Mày Không Vỡ Nắp Ơi!, 1934.
- Số Đỏ, 1936.
- Sư Cụ Triết Lý, 1935.
- Trúng Số Độc Đắc, 1938.
- Tự Do, 1937.
- Từ Lý Thuyết Đến Thực Hành, N/A.
- Vỡ Đê, 1936.
| ntt123/viet-tts-dataset | [
"license:cc-by-nc-4.0",
"region:us"
] | 2022-05-06T02:40:14+00:00 | {"license": "cc-by-nc-4.0"} | 2022-05-06T08:03:02+00:00 |
66f0eefe4b675a5d5411c7aa08e2c97fc9a9b17f | searle-j/kote | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:kor",
"license:mit",
"region:us"
] | 2022-05-06T04:55:04+00:00 | {"annotations_creators": ["crowdsourced"], "language": ["kor"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "multi-label-classification", "text-classification-other-emotion"], "pretty_name": "kote"} | 2022-10-20T18:16:24+00:00 |
|
01eda23ffaa04f414cb6044c014cb4ed317b3e38 | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mdroth/github-issues | [
"region:us"
] | 2022-05-06T07:27:56+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "due_on", "dtype": "null"}, {"name": "closed_at", "dtype": "null"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 4103283, "num_examples": 300}], "download_size": 866826, "dataset_size": 4103283}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-07-26T14:36:13+00:00 |
91c6572c454088bf71b679ad90aa8dffcd0d5868 |
# Dataset Card for MedMCQA
## 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://medmcqa.github.io
- **Repository:** https://github.com/medmcqa/medmcqa
- **Paper:** [MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://proceedings.mlr.press/v174/pal22a)
- **Leaderboard:** https://paperswithcode.com/dataset/medmcqa
- **Point of Contact:** [Aaditya Ura](mailto:aadityaura@gmail.com)
### Dataset Summary
MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.
MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity.
Each sample contains a question, correct answer(s), and other options which require a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study.
MedMCQA provides an open-source dataset for the Natural Language Processing community.
It is expected that this dataset would facilitate future research toward achieving better QA systems.
The dataset contains questions about the following topics:
- Anesthesia
- Anatomy
- Biochemistry
- Dental
- ENT
- Forensic Medicine (FM)
- Obstetrics and Gynecology (O&G)
- Medicine
- Microbiology
- Ophthalmology
- Orthopedics
- Pathology
- Pediatrics
- Pharmacology
- Physiology
- Psychiatry
- Radiology
- Skin
- Preventive & Social Medicine (PSM)
- Surgery
### Supported Tasks and Leaderboards
multiple-choice-QA, open-domain-QA: The dataset can be used to train a model for multi-choice questions answering, open domain questions answering. Questions in these exams are challenging and generally require deeper domain and language understanding as it tests the 10+ reasoning abilities across a wide range of medical subjects & topics.
### Languages
The questions and answers are available in English.
## Dataset Structure
### Data Instances
```
{
"question":"A 40-year-old man presents with 5 days of productive cough and fever. Pseudomonas aeruginosa is isolated from a pulmonary abscess. CBC shows an acute effect characterized by marked leukocytosis (50,000 mL) and the differential count reveals a shift to left in granulocytes. Which of the following terms best describes these hematologic findings?",
"exp": "Circulating levels of leukocytes and their precursors may occasionally reach very high levels (>50,000 WBC mL). These extreme elevations are sometimes called leukemoid reactions because they are similar to the white cell counts observed in leukemia, from which they must be distinguished. The leukocytosis occurs initially because of the accelerated release of granulocytes from the bone marrow (caused by cytokines, including TNF and IL-1) There is a rise in the number of both mature and immature neutrophils in the blood, referred to as a shift to the left. In contrast to bacterial infections, viral infections (including infectious mononucleosis) are characterized by lymphocytosis Parasitic infestations and certain allergic reactions cause eosinophilia, an increase in the number of circulating eosinophils. Leukopenia is defined as an absolute decrease in the circulating WBC count.",
"cop":1,
"opa":"Leukemoid reaction",
"opb":"Leukopenia",
"opc":"Myeloid metaplasia",
"opd":"Neutrophilia",
"subject_name":"Pathology",
"topic_name":"Basic Concepts and Vascular changes of Acute Inflammation",
"id":"4e1715fe-0bc3-494e-b6eb-2d4617245aef",
"choice_type":"single"
}
```
### Data Fields
- `id` : a string question identifier for each example
- `question` : question text (a string)
- `opa` : Option A
- `opb` : Option B
- `opc` : Option C
- `opd` : Option D
- `cop` : Correct option, i.e., 1,2,3,4
- `choice_type` ({"single", "multi"}): Question choice type.
- "single": Single-choice question, where each choice contains a single option.
- "multi": Multi-choice question, where each choice contains a combination of multiple suboptions.
- `exp` : Expert's explanation of the answer
- `subject_name` : Medical Subject name of the particular question
- `topic_name` : Medical topic name from the particular subject
### Data Splits
The goal of MedMCQA is to emulate the rigor of real word medical exams. To enable that, a predefined split of the dataset is provided. The split is by exams instead of the given questions. This also ensures the reusability and generalization ability of the models.
The training set of MedMCQA consists of all the collected mock & online test series, whereas the test set consists of all AIIMS PG exam MCQs (years 1991-present). The development set consists of NEET PG exam MCQs (years 2001-present) to approximate real exam evaluation.
Similar questions from train , test and dev set were removed based on similarity. The final split sizes are as follow:
| | Train | Test | Valid |
| ----- | ------ | ----- | ---- |
| Question #| 182,822 | 6,150 | 4,183|
| Vocab | 94,231 | 11,218 | 10,800 |
| Max Ques tokens | 220 | 135| 88 |
| Max Ans tokens | 38 | 21 | 25 |
## Dataset Creation
### Curation Rationale
Before this attempt, very few works have been done to construct biomedical MCQA datasets (Vilares and Gomez-Rodr, 2019), and they are (1) mostly small, containing up to few thousand questions, and (2) cover a limited number of Medical topics and Subjects. This paper addresses the aforementioned limitations by introducing MedMCQA, a new large-scale, Multiple-Choice Question Answering
(MCQA) dataset designed to address real-world medical entrance exam questions.
### Source Data
#### Initial Data Collection and Normalization
Historical Exam questions from official websites - AIIMS & NEET PG (1991- present)
The raw data is collected from open websites and books
#### Who are the source language producers?
The dataset was created by Ankit Pal, Logesh Kumar Umapathi and Malaikannan Sankarasubbu
### Annotations
#### Annotation process
The dataset does not contain any additional annotations.
#### 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
If you find this useful in your research, please consider citing the dataset paper
```
@InProceedings{pmlr-v174-pal22a,
title = {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
author = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
pages = {248--260},
year = {2022},
editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},
volume = {174},
series = {Proceedings of Machine Learning Research},
month = {07--08 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},
url = {https://proceedings.mlr.press/v174/pal22a.html},
abstract = {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study.}
}
```
### Contributions
Thanks to [@monk1337](https://github.com/monk1337) for adding this dataset. | medmcqa | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-05-06T07:43:24+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering", "multiple-choice"], "task_ids": ["multiple-choice-qa", "open-domain-qa"], "paperswithcode_id": "medmcqa", "pretty_name": "MedMCQA", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "opa", "dtype": "string"}, {"name": "opb", "dtype": "string"}, {"name": "opc", "dtype": "string"}, {"name": "opd", "dtype": "string"}, {"name": "cop", "dtype": {"class_label": {"names": {"0": "a", "1": "b", "2": "c", "3": "d"}}}}, {"name": "choice_type", "dtype": "string"}, {"name": "exp", "dtype": "string"}, {"name": "subject_name", "dtype": "string"}, {"name": "topic_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 131903297, "num_examples": 182822}, {"name": "test", "num_bytes": 1399350, "num_examples": 6150}, {"name": "validation", "num_bytes": 2221428, "num_examples": 4183}], "download_size": 88311487, "dataset_size": 135524075}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]} | 2024-01-04T14:32:02+00:00 |
67e4d8c2570caef0f90d48fdb756b337875d91db |
# Freesound Dataset 50k (FSD50K)
## Important
**This data set is a copy from the original one located at Zenodo.**
## Dataset Description
- **Homepage:** [FSD50K](https://zenodo.org/record/4060432)
- **Repository:** [GitHub](https://github.com/edufonseca/FSD50K_baseline)
- **Paper:** [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://arxiv.org/abs/2010.00475)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/fsd50k)
## Citation
If you use the FSD50K dataset, or part of it, please cite our paper:
>Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, Xavier Serra. "FSD50K: an Open Dataset of Human-Labeled Sound Events", arXiv 2020.
### Data curators
Eduardo Fonseca, Xavier Favory, Jordi Pons, Mercedes Collado, Ceren Can, Rachit Gupta, Javier Arredondo, Gary Avendano and Sara Fernandez
### Contact
You are welcome to contact Eduardo Fonseca should you have any questions at eduardo.fonseca@upf.edu.
## About FSD50K
Freesound Dataset 50k (or **FSD50K** for short) is an open dataset of human-labeled sound events containing 51,197 <a href="https://freesound.org/">Freesound</a> clips unequally distributed in 200 classes drawn from the <a href="https://research.google.com/audioset/ontology/index.html">AudioSet Ontology</a> [1]. FSD50K has been created at the <a href="https://www.upf.edu/web/mtg">Music Technology Group of Universitat Pompeu Fabra</a>.
What follows is a brief summary of FSD50K's most important characteristics. Please have a look at our paper (especially Section 4) to extend the basic information provided here with relevant details for its usage, as well as discussion, limitations, applications and more.
**Basic characteristics:**
- FSD50K is composed mainly of sound events produced by physical sound sources and production mechanisms.
- Following AudioSet Ontology’s main families, the FSD50K vocabulary encompasses mainly *Human sounds*, *Sounds of things*, *Animal*, *Natural sounds* and *Music*.
- The dataset has 200 sound classes (144 leaf nodes and 56 intermediate nodes) hierarchically organized with a subset of the AudioSet Ontology. The vocabulary can be inspected in `vocabulary.csv` (see Files section below).
- FSD50K contains 51,197 audio clips totalling 108.3 hours of audio.
- The audio content has been manually labeled by humans following a data labeling process using the <a href="https://annotator.freesound.org/">Freesound Annotator</a> platform [2].
- Clips are of variable length from 0.3 to 30s, due to the diversity of the sound classes and the preferences of Freesound users when recording sounds.
- Ground truth labels are provided at the clip-level (i.e., weak labels).
- The dataset poses mainly a multi-label sound event classification problem (but also allows a variety of sound event research tasks, see Sec. 4D).
- All clips are provided as uncompressed PCM 16 bit 44.1 kHz mono audio files.
- The audio clips are grouped into a development (*dev*) set and an evaluation (*eval*) set such that they do not have clips from the same Freesound uploader.
**Dev set:**
- 40,966 audio clips totalling 80.4 hours of audio
- Avg duration/clip: 7.1s
- 114,271 smeared labels (i.e., labels propagated in the upwards direction to the root of the ontology)
- Labels are correct but could be occasionally incomplete
- A train/validation split is provided (Sec. 3H). If a different split is used, it should be specified for reproducibility and fair comparability of results (see Sec. 5C of our paper)
**Eval set:**
- 10,231 audio clips totalling 27.9 hours of audio
- Avg duration/clip: 9.8s
- 38,596 smeared labels
- Eval set is labeled exhaustively (labels are correct and complete for the considered vocabulary)
**NOTE:** All classes in FSD50K are represented in AudioSet, except `Crash cymbal`, `Human group actions`, `Human voice`, `Respiratory sounds`, and `Domestic sounds, home sounds`.
## License
All audio clips in FSD50K are released under Creative Commons (CC) licenses. Each clip has its own license as defined by the clip uploader in Freesound, some of them requiring attribution to their original authors and some forbidding further commercial reuse. For attribution purposes and to facilitate attribution of these files to third parties, we include a mapping from the audio clips to their corresponding licenses. The licenses are specified in the files `dev_clips_info_FSD50K.json` and `eval_clips_info_FSD50K.json`. These licenses are CC0, CC-BY, CC-BY-NC and CC Sampling+.
In addition, FSD50K as a whole is the result of a curation process and it has an additional license: FSD50K is released under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY</a>. This license is specified in the `LICENSE-DATASET` file downloaded with the `FSD50K.doc` zip file.
## Files
FSD50K can be downloaded as a series of zip files with the following directory structure:
<div class="highlight"><pre><span></span>root
│
└───clips/ Audio clips
│ │
│ └─── dev/ Audio clips in the dev set
│ │
│ └─── eval/ Audio clips in the eval set
│
└───labels/ Files for FSD50K's ground truth
│ │
│ └─── dev.csv Ground truth for the dev set
│ │
│ └─── eval.csv Ground truth for the eval set
│ │
│ └─── vocabulary.csv List of 200 sound classes in FSD50K
│
└───metadata/ Files for additional metadata
│ │
│ └─── class_info_FSD50K.json Metadata about the sound classes
│ │
│ └─── dev_clips_info_FSD50K.json Metadata about the dev clips
│ │
│ └─── eval_clips_info_FSD50K.json Metadata about the eval clips
│ │
│ └─── pp_pnp_ratings_FSD50K.json PP/PNP ratings
│ │
│ └─── collection/ Files for the *sound collection* format
│
│
└───README.md The dataset description file that you are reading
│
└───LICENSE-DATASET License of the FSD50K dataset as an entity
</pre></div>
Each row (i.e. audio clip) of `dev.csv` contains the following information:
- `fname`: the file name without the `.wav` extension, e.g., the fname `64760` corresponds to the file `64760.wav` in disk. This number is the Freesound id. We always use Freesound ids as filenames.
- `labels`: the class labels (i.e., the ground truth). Note these class labels are *smeared*, i.e., the labels have been propagated in the upwards direction to the root of the ontology. More details about the label smearing process can be found in Appendix D of our paper.
- `mids`: the Freebase identifiers corresponding to the class labels, as defined in the <a href="https://github.com/audioset/ontology/blob/master/ontology.json">AudioSet Ontology specification</a>
- `split`: whether the clip belongs to *train* or *val* (see paper for details on the proposed split)
Rows in `eval.csv` follow the same format, except that there is no `split` column.
**NOTE:** We use a slightly different format than AudioSet for the naming of class labels in order to avoid potential problems with spaces, commas, etc. Example: we use `Accelerating_and_revving_and_vroom` instead of the original `Accelerating, revving, vroom`. You can go back to the original AudioSet naming using the information provided in `vocabulary.csv` (class label and mid for the 200 classes of FSD50K) and the <a href="https://github.com/audioset/ontology/blob/master/ontology.json">AudioSet Ontology specification</a>.
### Files with additional metadata (metadata/)
To allow a variety of analysis and approaches with FSD50K, we provide the following metadata:
1. `class_info_FSD50K.json`: python dictionary where each entry corresponds to one sound class and contains: `FAQs` utilized during the annotation of the class, `examples` (representative audio clips), and `verification_examples` (audio clips presented to raters during annotation as a quality control mechanism). Audio clips are described by the Freesound id.
**NOTE:** It may be that some of these examples are not included in the FSD50K release.
2. `dev_clips_info_FSD50K.json`: python dictionary where each entry corresponds to one dev clip and contains: title, description, tags, clip license, and the uploader name. All these metadata are provided by the uploader.
3. `eval_clips_info_FSD50K.json`: same as before, but with eval clips.
4. `pp_pnp_ratings.json`: python dictionary where each entry corresponds to one clip in the dataset and contains the PP/PNP ratings for the labels associated with the clip. More specifically, these ratings are gathered for the labels validated in **the validation task** (Sec. 3 of paper). This file includes 59,485 labels for the 51,197 clips in FSD50K. Out of these labels:
- 56,095 labels have inter-annotator agreement (PP twice, or PNP twice). Each of these combinations can be occasionally accompanied by other (non-positive) ratings.
- 3390 labels feature other rating configurations such as *i)* only one PP rating and one PNP rating (and nothing else). This can be considered inter-annotator agreement at the ``Present” level; *ii)* only one PP rating (and nothing else); *iii)* only one PNP rating (and nothing else).
Ratings' legend: PP=1; PNP=0.5; U=0; NP=-1.
**NOTE:** The PP/PNP ratings have been provided in the *validation* task. Subsequently, a subset of these clips corresponding to the eval set was exhaustively labeled in the *refinement* task, hence receiving additional labels in many cases. For these eval clips, you might want to check their labels in `eval.csv` in order to have more info about their audio content (see Sec. 3 for details).
5. `collection/`: This folder contains metadata for what we call the ***sound collection format***. This format consists of the raw annotations gathered, featuring all generated class labels without any restriction.
We provide the *collection* format to make available some annotations that do not appear in the FSD50K *ground truth* release. This typically happens in the case of classes for which we gathered human-provided annotations, but that were discarded in the FSD50K release due to data scarcity (more specifically, they were merged with their parents). In other words, the main purpose of the `collection` format is to make available annotations for tiny classes. The format of these files in analogous to that of the files in `FSD50K.ground_truth/`. A couple of examples show the differences between **collection** and **ground truth** formats:
`clip`: `labels_in_collection` -- `labels_in_ground_truth`
`51690`: `Owl` -- `Bird,Wild_Animal,Animal`
`190579`: `Toothbrush,Electric_toothbrush` -- `Domestic_sounds_and_home_sounds`
In the first example, raters provided the label `Owl`. However, due to data scarcity, `Owl` labels were merged into their parent `Bird`. Then, labels `Wild_Animal,Animal` were added via label propagation (smearing). The second example shows one of the most extreme cases, where raters provided the labels `Electric_toothbrush,Toothbrush`, which both had few data. Hence, they were merged into Toothbrush's parent, which unfortunately is `Domestic_sounds_and_home_sounds` (a rather vague class containing a variety of children sound classes).
**NOTE:** Labels in the collection format are not smeared.
**NOTE:** While in FSD50K's ground truth the vocabulary encompasses 200 classes (common for dev and eval), since the *collection* format is composed of raw annotations, the vocabulary here is much larger (over 350 classes), and it is slightly different in dev and eval.
For further questions, please contact eduardo.fonseca@upf.edu, or join the <a href="https://groups.google.com/g/freesound-annotator">freesound-annotator Google Group</a>.
## Download
Clone this repository:
```
git clone https://huggingface.co/Fhrozen/FSD50k
```
## Baseline System
Several baseline systems for FSD50K are available at <a href="https://github.com/edufonseca/FSD50K_baseline">https://github.com/edufonseca/FSD50K_baseline</a>. The experiments are described in Sec 5 of our paper.
## References and links
[1] Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. "Audio set: An ontology and human-labeled dataset for audio events." In Proceedings of the International Conference on Acoustics, Speech and Signal Processing, 2017. [<a href="https://ai.google/research/pubs/pub45857">PDF</a>]
[2] Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra. "Freesound Datasets: A Platform for the Creation of Open Audio Datasets." In Proceedings of the International Conference on Music Information Retrieval, 2017. [<a href="https://repositori.upf.edu/bitstream/handle/10230/33299/fonseca_ismir17_freesound.pdf">PDF</a>]
Companion site for FSD50K: <a href="https://annotator.freesound.org/fsd/release/FSD50K/">https://annotator.freesound.org/fsd/release/FSD50K/</a>
Freesound Annotator: <a href="https://annotator.freesound.org/">https://annotator.freesound.org/</a>
Freesound: <a href="https://freesound.org">https://freesound.org</a>
Eduardo Fonseca's personal website: <a href="http://www.eduardofonseca.net/">http://www.eduardofonseca.net/</a>
More datasets collected by us: <a href="http://www.eduardofonseca.net/datasets/">http://www.eduardofonseca.net/datasets/</a>
## Acknowledgments
The authors would like to thank everyone who contributed to FSD50K with annotations, and especially Mercedes Collado, Ceren Can, Rachit Gupta, Javier Arredondo, Gary Avendano and Sara Fernandez for their commitment and perseverance. The authors would also like to thank Daniel P.W. Ellis and Manoj Plakal from Google Research for valuable discussions. This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 <a href="https://www.audiocommons.org/">AudioCommons</a>, and two Google Faculty Research Awards <a href="https://ai.googleblog.com/2018/03/google-faculty-research-awards-2017.html">2017</a> and <a href="https://ai.googleblog.com/2019/03/google-faculty-research-awards-2018.html">2018</a>, and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
| Fhrozen/FSD50k | [
"task_categories:audio-classification",
"annotations_creators:unknown",
"language_creators:unknown",
"size_categories:10K<n<100K",
"source_datasets:unknown",
"license:cc-by-4.0",
"arxiv:2010.00475",
"region:us"
] | 2022-05-06T07:51:56+00:00 | {"annotations_creators": ["unknown"], "language_creators": ["unknown"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "source_datasets": ["unknown"], "task_categories": ["audio-classification"], "task_ids": ["other-audio-slot-filling"]} | 2022-05-27T07:50:25+00:00 |
a9d58d45d5363ececbe0485f26350fff6835f611 |
# Dataset Card for MNIST
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://yann.lecun.com/exdb/mnist/
- **Repository:**
- **Paper:** MNIST handwritten digit database by Yann LeCun, Corinna Cortes, and CJ Burges
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.
Half of the image were drawn by Census Bureau employees and the other half by high school students (this split is evenly distributed in the training and testing sets).
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-mnist).
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its label:
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x276021F6DD8>,
'label': 5
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `label`: an integer between 0 and 9 representing the digit.
### Data Splits
The data is split into training and test set. All the images in the test set were drawn by different individuals than the images in the training set. The training set contains 60,000 images and the test set 10,000 images.
## Dataset Creation
### Curation Rationale
The MNIST database was created to provide a testbed for people wanting to try pattern recognition methods or machine learning algorithms while spending minimal efforts on preprocessing and formatting. Images of the original dataset (NIST) were in two groups, one consisting of images drawn by Census Bureau employees and one consisting of images drawn by high school students. In NIST, the training set was built by grouping all the images of the Census Bureau employees, and the test set was built by grouping the images form the high school students.
The goal in building MNIST was to have a training and test set following the same distributions, so the training set contains 30,000 images drawn by Census Bureau employees and 30,000 images drawn by high school students, and the test set contains 5,000 images of each group. The curators took care to make sure all the images in the test set were drawn by different individuals than the images in the training set.
### Source Data
#### Initial Data Collection and Normalization
The original images from NIST were size normalized to fit a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels (i.e., pixels don't simply have a value of black and white, but a level of greyness from 0 to 255) as a result of the anti-aliasing technique used by the normalization algorithm. The images were then centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.
#### Who are the source language producers?
Half of the source images were drawn by Census Bureau employees, half by high school students. According to the dataset curator, the images from the first group are more easily recognizable.
### Annotations
#### Annotation process
The images were not annotated after their creation: the image creators annotated their images with the corresponding label after drawing them.
#### Who are the annotators?
Same as the source data creators.
### 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
Chris Burges, Corinna Cortes and Yann LeCun
### Licensing Information
MIT Licence
### Citation Information
```
@article{lecun2010mnist,
title={MNIST handwritten digit database},
author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
volume={2},
year={2010}
}
```
### Contributions
Thanks to [@sgugger](https://github.com/sgugger) for adding this dataset.
| filwsyl/video_tags | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-nist",
"language:enx",
"license:mit",
"region:us"
] | 2022-05-06T08:19:54+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["enx"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-nist"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "mnist", "pretty_name": "MNIST"} | 2022-10-25T09:13:17+00:00 |
36d51f10c05d1598552a0374b04d7b8e58efddbc |
# KPTimes Benchmark Dataset for Keyphrase Generation
## About
KPTimes is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 290K news articles in English collected from the [New York Times](https://www.nytimes.com/) and the [Japan
Times](https://www.japantimes.co.jp/).
Keyphrases were annotated by editors in a semi-automated manner (that is, editors revise a set of keyphrases proposed by an algorithm and provide additional keyphrases).
Details about the dataset can be found in the original paper [(Gallina et al., 2019)][gallina-2019].
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].
Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`. <u>P</u>resent keyphrases are ordered according to their first occurrence position in the text.
## Content and statistics
The dataset contains the following test split:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
| Train | 259,923 | 921 | 5.03 | 45.61 | 15.57 | 29.63 | 9.19 |
| Validation | 10,000 | 921 | 5.02 | 45.22 | 15.78 | 29.60 | 9.41 |
| Test | 20,000 | 648 | 5.03 | 60.64 | 8.90 | 18.95 | 11.51 |
The following data fields are available :
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
- **date**: publishing date (YYYY/MM/DD)
- **categories**: categories of the article (1 or 2 categories)
## References
- (Gallina et al., 2019) Ygor Gallina, Florian Boudin, and Beatrice Daille. 2019.
[KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents][gallina-2019].
In Proceedings of the 12th International Conference on Natural Language Generation, pages 130–135, Tokyo, Japan. Association for Computational Linguistics.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021].
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
[gallina-2019]: https://aclanthology.org/W19-8617/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/ | taln-ls2n/kptimes | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-05-06T08:34:40+00:00 | {"annotations_creators": ["unknown"], "language_creators": ["unknown"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["text-mining", "text-generation"], "task_ids": ["keyphrase-generation", "keyphrase-extraction"], "pretty_name": "KPTimes"} | 2022-09-23T06:38:28+00:00 |
da97033c65ab45c0f6735cfa5b9c18ff8e9f1bde | languages:
- en
task_categories:
- translation
licenses:
- unknown
# Dataset Card for [Needs More Information]
## 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
This is a dataset made up of two Bible translations-- NET and KJV.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
The original intention is to use the dataset to "translate" between modern and 17th-century English. By doing so, we can potentially read understand things from that period more clearly.
### 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
Before the 18th and 19th centuries, English spelling was inconsistent. Because of this, the model often does not recognize spellings different from those in the KJV.
The model was trained on a relatively small amount of data, so it will not be as accurate as a model trained on a larger data set.
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | swcrazyfan/net-kjv | [
"region:us"
] | 2022-05-06T08:43:22+00:00 | {} | 2022-05-06T09:05:48+00:00 |
b603503ce82cf503a03ffa407feb2c17d4f4c27a | vehnem/DBpedia | [
"license:cc-by-sa-3.0",
"region:us"
] | 2022-05-06T10:01:55+00:00 | {"license": "cc-by-sa-3.0"} | 2022-05-06T10:01:55+00:00 |
|
cfe049cf2184769741166b8b369798bbe3dafb70 | # Aurora SDGs Dataset
This data set contains metdata of 1.4 million research papers. The abstract plus the SDG labels for the Goals and Targets. | MauriceV2021/AuroraSDGsDataset | [
"license:cc-by-4.0",
"region:us"
] | 2022-05-06T10:23:04+00:00 | {"license": "cc-by-4.0"} | 2022-05-06T10:24:47+00:00 |
9e3261d54d2c334e495dc6cb6fbd8fe99b13c2ac |
# Dataset Card for ASCEND
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/2112.06223
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Chinese and English
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | filwsyl/ascend | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"arxiv:2112.06223",
"region:us"
] | 2022-05-06T10:42:28+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en", "zh"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition"], "task_ids": ["code-switching", "speech-recognition"], "pretty_name": "ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation", "language_bcp47": ["en", "zh-CN"]} | 2022-10-25T04:24:45+00:00 |
e418c1fc928d9f5393af33268472cf20c1891be8 |
# Dataset Card for Aksharantar
## 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://indicnlp.ai4bharat.org/indic-xlit/
- **Repository:** https://github.com/AI4Bharat/IndicXlit/
- **Paper:** [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Aksharantar is the largest publicly available transliteration dataset for 20 Indic languages. The corpus has 26M Indic language-English transliteration pairs.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
| <!-- --> | <!-- --> | <!-- --> | <!-- --> | <!-- --> | <!-- --> |
| -------------- | -------------- | -------------- | --------------- | -------------- | ------------- |
| Assamese (asm) | Hindi (hin) | Maithili (mai) | Marathi (mar) | Punjabi (pan) | Tamil (tam) |
| Bengali (ben) | Kannada (kan) | Malayalam (mal)| Nepali (nep) | Sanskrit (san) | Telugu (tel) |
| Bodo(brx) | Kashmiri (kas) | Manipuri (mni) | Oriya (ori) | Sindhi (snd) | Urdu (urd) |
| Gujarati (guj) | Konkani (kok) | Dogri (doi) |
## Dataset Structure
### Data Instances
```
A random sample from Hindi (hin) Train dataset.
{
'unique_identifier': 'hin1241393',
'native word': 'स्वाभिमानिक',
'english word': 'swabhimanik',
'source': 'IndicCorp',
'score': -0.1028788579
}
```
### Data Fields
- `unique_identifier` (string): 3-letter language code followed by a unique number in each set (Train, Test, Val).
- `native word` (string): A word in Indic language.
- `english word` (string): Transliteration of native word in English (Romanised word).
- `source` (string): Source of the data.
- `score` (num): Character level log probability of indic word given roman word by IndicXlit (model). Pairs with average threshold of the 0.35 are considered.
For created data sources, depending on the destination/sampling method of a pair in a language, it will be one of:
- Dakshina Dataset
- IndicCorp
- Samanantar
- Wikidata
- Existing sources
- Named Entities Indian (AK-NEI)
- Named Entities Foreign (AK-NEF)
- Data from Uniform Sampling method. (Ak-Uni)
- Data from Most Frequent words sampling method. (Ak-Freq)
### Data Splits
| Subset | asm-en | ben-en | brx-en | guj-en | hin-en | kan-en | kas-en | kok-en | mai-en | mal-en | mni-en | mar-en | nep-en | ori-en | pan-en | san-en | sid-en | tam-en | tel-en | urd-en |
|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| Training | 179K | 1231K | 36K | 1143K | 1299K | 2907K | 47K | 613K | 283K | 4101K | 10K | 1453K | 2397K | 346K | 515K | 1813K | 60K | 3231K | 2430K | 699K |
| Validation | 4K | 11K | 3K | 12K | 6K | 7K | 4K | 4K | 4K | 8K | 3K | 8K | 3K | 3K | 9K | 3K | 8K | 9K | 8K | 12K |
| Test | 5531 | 5009 | 4136 | 7768 | 5693 | 6396 | 7707 | 5093 | 5512 | 6911 | 4925 | 6573 | 4133 | 4256 | 4316 | 5334 | - | 4682 | 4567 | 4463 |
## Dataset Creation
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
#### Who are the source language producers?
[More Information Needed]
### Annotations
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
#### Annotation process
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
#### Who are the annotators?
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
### 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
<!-- <a rel="license" float="left" href="http://creativecommons.org/publicdomain/zero/1.0/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100" />
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100" href="http://creativecommons.org/publicdomain/zero/1.0/"/>
</a>
<br/> -->
This data is released under the following licensing scheme:
- Manually collected data: Released under CC-BY license.
- Mined dataset (from Samanantar and IndicCorp): Released under CC0 license.
- Existing sources: Released under CC0 license.
**CC-BY License**
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100"/>
</a>
<br>
<br>
<!--
and the Aksharantar benchmark and all manually transliterated data under the [Creative Commons CC-BY license (“no rights reserved”)](https://creativecommons.org/licenses/by/4.0/). -->
**CC0 License Statement**
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100"/>
</a>
<br>
<br>
- We do not own any of the text from which this data has been extracted.
- We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0).
- To the extent possible under law, <a rel="dct:publisher" href="https://indicnlp.ai4bharat.org/aksharantar/"> <span property="dct:title">AI4Bharat</span></a> has waived all copyright and related or neighboring rights to <span property="dct:title">Aksharantar</span> manually collected data and existing sources.
- This work is published from: India.
### Citation Information
```
@misc{madhani2022aksharantar,
title={Aksharantar: Towards Building Open Transliteration Tools for the Next Billion Users},
author={Yash Madhani and Sushane Parthan and Priyanka Bedekar and Ruchi Khapra and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra},
year={2022},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions | ai4bharat/Aksharantar | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:found",
"language_creators:other",
"multilinguality:multilingual",
"source_datasets:original",
"language:asm",
"language:ben",
"language:brx",
"language:doi",
"language:guj",
"language:hin",
"language:kan",
"language:kas",
"language:kok",
"language:mai",
"language:mal",
"language:mar",
"language:mni",
"language:nep",
"language:ori",
"language:pan",
"language:san",
"language:sid",
"language:tam",
"language:tel",
"language:urd",
"license:cc",
"arxiv:2205.03018",
"region:us"
] | 2022-05-06T11:35:15+00:00 | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated", "machine-generated", "found", "other"], "language": ["asm", "ben", "brx", "doi", "guj", "hin", "kan", "kas", "kok", "mai", "mal", "mar", "mni", "nep", "ori", "pan", "san", "sid", "tam", "tel", "urd"], "license": "cc", "multilinguality": ["multilingual"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": [], "pretty_name": "Aksharantar"} | 2023-08-31T06:05:34+00:00 |
815620f1e0dbeaa8958d7101777047ed24a9cbbd | # Full FLIP stability dataset
The stability dataset from flip, which is based on the meltome atlas, data has those columns:
```
[ 'index', 'seq_id', 'sequence', 'target', 'cluster_center',
'cluster_distance']
```
- **Index** from the original dataset
- **Seq_id** a unique sequence ID string that is concatenated from several other IDs (also Unirep)
- **Sequence** The actual protein sequence as a string
- **Target** the melting point temperature of the protein TM
- **Cluster center** The seq_id of the cluster center protein this sequence is assigned to. Can also be its won seq_id if this sequence is a center.
- **Cluster distance** The levenstein distance of the protein to its cluster center. | cradle-bio/FLIP_clusters | [
"region:us"
] | 2022-05-06T12:21:38+00:00 | {} | 2022-05-06T12:29:51+00:00 |
6348a19fb3d22aa7fd90b7c12e17969056839c05 |
Use it as usual:
```python
ds = load_dataset("polinaeterna/vox_lingua", "sco")
```
If you want to download all the languages, use `"all"` config:
```python
ds = load_dataset("polinaeterna/vox_lingua", "all")
``` | polinaeterna/vox_lingua | [
"license:cc-by-4.0",
"region:us"
] | 2022-05-06T14:26:59+00:00 | {"license": "cc-by-4.0"} | 2022-12-06T11:09:02+00:00 |
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