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"benchmark",
"region:us"
] | 2022-03-25T14:54:37Z | 2022-03-25T14:54:36.000Z | 2022-03-25T14:54:36 | ---
benchmark: gem
type: prediction
submission_name: This is a test name
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test name
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license: cc-by-4.0
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jiangjiechen/ekar_chinese | jiangjiechen | 2023-01-11T08:12:59Z | 14 | 10 | null | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_ids:explanation-generation",
"size_categories:1K<n<2K",
"source_datasets:original",
"language:zh",
"license:afl-3.0",
"region:us"
] | 2023-01-11T08:12:59Z | 2022-03-27T06:00:49.000Z | 2022-03-27T06:00:49 | ---
language:
- zh
license:
- afl-3.0
size_categories:
- 1K<n<2K
source_datasets:
- original
task_categories:
- question-answering
- text-generation
task_ids:
- analogical-qa
- explanation-generation
---
# Dataset Card for ekar_chinese
## 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://ekar-leaderboard.github.io
- **Paper:** [E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning](https://aclanthology.org/2022.findings-acl.311)
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1671/overview
- **Point of Contact:** jjchen19@fudan.edu.cn
### Dataset Summary
***New!***(9/18/2022) E-KAR `v1.1` is officially released (at the `main` branch), **with a higher-quality English dataset!** In `v1.1`, we further improve the Chinese-to-English translation quality of the English E-KAR, with over 600 problems and over 1,000 explanations manually adjusted. You can still find previous version (as in the paper) in the `v1.0` branch in the repo. For more information please refer to https://ekar-leaderboard.github.io.
The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in English) problems sourced from the Civil Service Exams, which require intensive background knowledge to solve. More importantly, we design a free-text explanation scheme to explain whether an analogy should be drawn, and manually annotate them for each and every question and candidate answer. Empirical results suggest that this benchmark is very challenging for some state-of-the-art models for both explanation generation and analogical question answering tasks, which invites further research in this area.
### Supported Tasks and Leaderboards
- `analogical-qa`: The dataset can be used to train a model for analogical reasoning in the form of multiple-choice QA.
- `explanation-generation`: The dataset can be used to generate free-text explanations to rationalize analogical reasoning.
This dataset supports two task modes: EASY mode and HARD mode:
- `EASY mode`: where query explanation can be used as part of the input.
- `HARD mode`: no explanation is allowed as part of the input.
### Languages
This dataset is in Chinese, with its [English version](https://huggingface.co/datasets/Jiangjie/ekar_english).
## Dataset Structure
### Data Instances
```json
{
"id": "982f17-en",
"question": "plant:coal",
"choices": {
"label": [
"A",
"B",
"C",
"D"
],
"text": [
"white wine:aged vinegar",
"starch:corn",
"milk:yogurt",
"pickled cabbage:cabbage"
]
},
"answerKey": "C",
"explanation": [
"\"plant\" is the raw material of \"coal\".",
"both \"white wine\" and \"aged vinegar\" are brewed.",
"\"starch\" is made of \"corn\", and the order of words is inconsistent with the query.",
"\"yogurt\" is made from \"milk\".",
"\"pickled cabbage\" is made of \"cabbage\", and the word order is inconsistent with the query."
],
"relation": [
[["plant", "coal", "R3.7"]],
[["white wine", "aged vinegar", "R2.4"]],
[["corn", "starch", "R3.7"]],
[["milk", "yogurt", "R3.7"]],
[["cabbage", "pickled cabbage", "R3.7"]]
]
}
```
### Data Fields
- id: a string identifier for each example.
- question: query terms.
- choices: candidate answer terms.
- answerKey: correct answer.
- explanation: explanations for query (1st) and candidate answers (2nd-5th).
- relation: annotated relations for terms in the query (1st) and candidate answers (2nd-5th).
### Data Splits
| name |train|validation|test|
|:-----:|:---:|:--------:|:--:|
|default| 1155 | 165 | 335 |
|description| | | blinded |
## 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
The purpose of this dataset is to help develop analogical reasoning systems that are right for the right reasons.
### Discussion of Biases
This dataset is sourced and translated from the Civil Service Examinations of China. Therefore, it may contain information biased to Chinese culture.
### Other Known Limitations
1. The explanation annotation process in E-KAR (not the EG task) is mostly post-hoc and reflects only the result of reasoning. Humans solve the analogy problems in a trial-and-error manner, i.e., adjusting the abduced source structure and trying to find the most suited one for all candidate answers. Therefore, such explanations cannot offer supervision for intermediate reasoning.
2. E-KAR only presents one feasible explanation for each problem, whereas there may be several.
## Additional Information
### Dataset Curators
The dataset was initially created and curated by Jiangjie Chen (Fudan University, ByteDance), Rui Xu (Fudan University), Ziquan Fu (Brain Technologies, Inc.), Wei Shi (South China University of Technology), Xinbo Zhang (ByteDance), Changzhi Sun (ByteDance) and other colleagues at ByteDance and Fudan University.
### Licensing Information
[Needs More Information]
### Citation Information
```latex
@inproceedings{chen-etal-2022-e,
title = "{E}-{KAR}: A Benchmark for Rationalizing Natural Language Analogical Reasoning",
author = "Chen, Jiangjie and
Xu, Rui and
Fu, Ziquan and
Shi, Wei and
Li, Zhongqiao and
Zhang, Xinbo and
Sun, Changzhi and
Li, Lei and
Xiao, Yanghua and
Zhou, Hao",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.311",
pages = "3941--3955",
}
```
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jiangjiechen/ekar_english | jiangjiechen | 2023-01-11T08:13:18Z | 14 | 3 | null | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_ids:explanation-generation",
"size_categories:1K<n<2K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"region:us"
] | 2023-01-11T08:13:18Z | 2022-03-27T06:03:06.000Z | 2022-03-27T06:03:06 | ---
language:
- en
license:
- afl-3.0
size_categories:
- 1K<n<2K
source_datasets:
- original
task_categories:
- question-answering
- text-generation
task_ids:
- analogical-qa
- explanation-generation
---
# Dataset Card for ekar_english
## 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://ekar-leaderboard.github.io
- **Paper:** [E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning](https://aclanthology.org/2022.findings-acl.311)
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1671/overview
- **Point of Contact:** jjchen19@fudan.edu.cn
### Dataset Summary
***New!***(9/18/2022) E-KAR `v1.1` is officially released (at the `main` branch), **with a higher-quality English dataset!** In `v1.1`, we further improve the Chinese-to-English translation quality of the English E-KAR, with over 600 problems and over 1,000 explanations manually adjusted. You can still find previous version (as in the paper) in the `v1.0` branch in the repo. For more information please refer to https://ekar-leaderboard.github.io.
The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in English) problems sourced from the Civil Service Exams, which require intensive background knowledge to solve. More importantly, we design a free-text explanation scheme to explain whether an analogy should be drawn, and manually annotate them for each and every question and candidate answer. Empirical results suggest that this benchmark is very challenging for some state-of-the-art models for both explanation generation and analogical question answering tasks, which invites further research in this area.
### Supported Tasks and Leaderboards
- `analogical-qa`: The dataset can be used to train a model for analogical reasoning in the form of multiple-choice QA.
- `explanation-generation`: The dataset can be used to generate free-text explanations to rationalize analogical reasoning.
This dataset supports two task modes: EASY mode and HARD mode:
- `EASY mode`: where query explanation can be used as part of the input.
- `HARD mode`: no explanation is allowed as part of the input.
### Languages
This dataset is in English, which is translated from [its Chinese version](https://huggingface.co/datasets/Jiangjie/ekar_chinese/)
## Dataset Structure
### Data Instances
```json
{
"id": "982f17-en",
"question": "plant:coal",
"choices": {
"label": [
"A",
"B",
"C",
"D"
],
"text": [
"white wine:aged vinegar",
"starch:corn",
"milk:yogurt",
"pickled cabbage:cabbage"
]
},
"answerKey": "C",
"explanation": [
"\"plant\" is the raw material of \"coal\".",
"both \"white wine\" and \"aged vinegar\" are brewed.",
"\"starch\" is made of \"corn\", and the order of words is inconsistent with the query.",
"\"yogurt\" is made from \"milk\".",
"\"pickled cabbage\" is made of \"cabbage\", and the word order is inconsistent with the query."
],
"relation": [
[["plant", "coal", "R3.7"]],
[["white wine", "aged vinegar", "R2.4"]],
[["corn", "starch", "R3.7"]],
[["milk", "yogurt", "R3.7"]],
[["cabbage", "pickled cabbage", "R3.7"]]
]
}
```
### Data Fields
- id: a string identifier for each example.
- question: query terms.
- choices: candidate answer terms.
- answerKey: correct answer.
- explanation: explanations for query (1st) and candidate answers (2nd-5th).
- relation: annotated relations for terms in the query (1st) and candidate answers (2nd-5th).
### Data Splits
| name |train|validation|test|
|:-----:|:---:|:--------:|:--:|
|default| 870| 119| 262|
|description| | | blinded |
## 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
The purpose of this dataset is to help develop analogical reasoning systems that are right for the right reasons.
### Discussion of Biases
This dataset is sourced and translated from the Civil Service Examinations of China. Therefore, despite the effort that the authors try to remove or rewrite such problems, it may still contain information biased to Chinese culture.
### Other Known Limitations
1. The explanation annotation process in E-KAR (not the EG task) is mostly post-hoc and reflects only the result of reasoning. Humans solve the analogy problems in a trial-and-error manner, i.e., adjusting the abduced source structure and trying to find the most suited one for all candidate answers. Therefore, such explanations cannot offer supervision for intermediate reasoning.
2. E-KAR only presents one feasible explanation for each problem, whereas there may be several.
3. The English version of E-KAR is machine-translated and post-edited by humans. Although the authors have tried their best to maintain the translation quality, there could be some unsatisfying samples in the English dataset, e.g., culture-specific ones, ambiguous ones after translation, etc.
## Additional Information
### Dataset Curators
The dataset was initially created and curated by Jiangjie Chen (Fudan University, ByteDance), Rui Xu (Fudan University), Ziquan Fu (Brain Technologies, Inc.), Wei Shi (South China University of Technology), Xinbo Zhang (ByteDance), Changzhi Sun (ByteDance) and other colleagues at ByteDance and Fudan University.
### Licensing Information
[Needs More Information]
### Citation Information
```latex
@inproceedings{chen-etal-2022-e,
title = "{E}-{KAR}: A Benchmark for Rationalizing Natural Language Analogical Reasoning",
author = "Chen, Jiangjie and
Xu, Rui and
Fu, Ziquan and
Shi, Wei and
Li, Zhongqiao and
Zhang, Xinbo and
Sun, Changzhi and
Li, Lei and
Xiao, Yanghua and
Zhou, Hao",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.311",
pages = "3941--3955",
}
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TzRain/AMPs | TzRain | 2022-03-31T07:17:11Z | 14 | 0 | null | [
"region:us"
] | 2022-03-31T07:17:11Z | 2022-03-27T11:04:47.000Z | 2022-03-27T11:04:47 | Entry not found | [
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stjokerli/TextToText_DocNLI_seqio | stjokerli | 2022-03-27T14:46:59Z | 14 | 0 | null | [
"region:us"
] | 2022-03-27T14:46:59Z | 2022-03-27T14:27:45.000Z | 2022-03-27T14:27:45 | text to text implementation basing on https://github.com/salesforce/DocNLI
DatasetDict({
train: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 942314
})
validation: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 234258
})
test: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 267086
})
}) | [
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stjokerli/TextToText_squad_seqio | stjokerli | 2022-03-27T22:39:25Z | 14 | 0 | null | [
"region:us"
] | 2022-03-27T22:39:25Z | 2022-03-27T22:28:53.000Z | 2022-03-27T22:28:53 | squad_v010_allanswers in T5 paper https://github.com/google-research/text-to-text-transfer-transformer/blob/main/t5/data/tasks.py
DatasetDict({
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validation: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 10570
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huggan/vangogh2photo | huggan | 2022-04-12T13:58:45Z | 14 | 0 | null | [
"arxiv:1703.10593",
"region:us"
] | 2022-04-12T13:58:45Z | 2022-03-29T12:33:03.000Z | 2022-03-29T12:33:03 | This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
# Citation
```
@article{DBLP:journals/corr/ZhuPIE17,
author = {Jun{-}Yan Zhu and
Taesung Park and
Phillip Isola and
Alexei A. Efros},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks},
journal = {CoRR},
volume = {abs/1703.10593},
year = {2017},
url = {http://arxiv.org/abs/1703.10593},
eprinttype = {arXiv},
eprint = {1703.10593},
timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
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huggan/apple2orange | huggan | 2022-04-12T13:55:40Z | 14 | 0 | null | [
"arxiv:1703.10593",
"region:us"
] | 2022-04-12T13:55:40Z | 2022-03-29T12:44:10.000Z | 2022-03-29T12:44:10 | This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
# Citation
```
@article{DBLP:journals/corr/ZhuPIE17,
author = {Jun{-}Yan Zhu and
Taesung Park and
Phillip Isola and
Alexei A. Efros},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks},
journal = {CoRR},
volume = {abs/1703.10593},
year = {2017},
url = {http://arxiv.org/abs/1703.10593},
eprinttype = {arXiv},
eprint = {1703.10593},
timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
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0.1436172127... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/iphone2dslr_flower | huggan | 2022-04-12T13:57:46Z | 14 | 0 | null | [
"arxiv:1703.10593",
"region:us"
] | 2022-04-12T13:57:46Z | 2022-03-29T12:47:17.000Z | 2022-03-29T12:47:17 | This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
# Citation
```
@article{DBLP:journals/corr/ZhuPIE17,
author = {Jun{-}Yan Zhu and
Taesung Park and
Phillip Isola and
Alexei A. Efros},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks},
journal = {CoRR},
volume = {abs/1703.10593},
year = {2017},
url = {http://arxiv.org/abs/1703.10593},
eprinttype = {arXiv},
eprint = {1703.10593},
timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
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huggan/grumpifycat | huggan | 2022-04-12T13:57:20Z | 14 | 0 | null | [
"arxiv:1703.10593",
"region:us"
] | 2022-04-12T13:57:20Z | 2022-03-29T14:42:02.000Z | 2022-03-29T14:42:02 | This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
# Citation
```
@article{DBLP:journals/corr/ZhuPIE17,
author = {Jun{-}Yan Zhu and
Taesung Park and
Phillip Isola and
Alexei A. Efros},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks},
journal = {CoRR},
volume = {abs/1703.10593},
year = {2017},
url = {http://arxiv.org/abs/1703.10593},
eprinttype = {arXiv},
eprint = {1703.10593},
timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
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rzhang123/UScourt | rzhang123 | 2022-03-31T19:57:31Z | 14 | 0 | null | [
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] | 2022-03-31T19:57:31Z | 2022-03-29T19:10:09.000Z | 2022-03-29T19:10:09 | Entry not found | [
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laion/laion1B-nolang-joined | laion | 2022-03-31T19:37:20Z | 14 | 0 | null | [
"region:us"
] | 2022-03-31T19:37:20Z | 2022-03-29T22:03:59.000Z | 2022-03-29T22:03:59 | Entry not found | [
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laion/laion1B-nolang-watermark | laion | 2022-03-30T18:18:02Z | 14 | 1 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-03-30T18:18:02Z | 2022-03-29T22:46:59.000Z | 2022-03-29T22:46:59 | ---
license: cc-by-4.0
---
| [
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hackathon-pln-es/nli-es | hackathon-pln-es | 2022-04-04T03:30:59Z | 14 | 2 | null | [
"arxiv:1809.05053",
"region:us"
] | 2022-04-04T03:30:59Z | 2022-03-29T23:54:07.000Z | 2022-03-29T23:54:07 | annotations_creators:
- crowdsourced
- other
language_creators:
- other
- crowdsourced
languages:
- es
licenses:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: ESnli
size_categories:
- unknown
source_datasets:
- extended|snli
- extended|xnli
- extended|multi_nli
task_categories:
- text-classification
task_ids:
- natural-language-inference
# Dataset Card for nli-es
## 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:** https://huggingface.co/datasets/hackathon-pln-es/nli-es/
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
A Spanish Natural Language Inference dataset put together from the sources:
- the Spanish slice of the XNLI dataset;
- machine-translated Spanish version of the SNLI dataset
- machine-translated Spanish version of the Multinli dataset
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
A small percentage of the dataset contains original Spanish text by human speakers. The rest was generated by automatic translation.
## Dataset Structure
### Data Instances
A line includes four values: a sentence1 (the premise); a sentence2 (the hypothesis); a label specifying the relationship between the two ("gold_label") and the ID number of the pair of sentences as given in the original dataset.
Labels can be "entailment" if the premise entails the hypothesis, "contradiction" if it contradicts it or "neutral" if it neither implies it nor denies it.
{
"gold_label": "neutral",
"pairID": 1,
"sentence1": "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos.",
"sentence2": "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario."
}
### Data Fields
gold_label: A string defining the relation between the sentence pair. Labels can be "entailment" if the premise entails the hypothesis, "contradiction" if it contradicts it or "neutral" if it neither implies it nor denies it.
pairID: A string identifying a pair sentence. It was inherited from the original datasets. NOTE: For the moment we are having trouble loading this column so we replaced every string with an int 0 as a placeholder. We hope to have the pairID back up soon.
sentence1: A string containing one sentence in Spanish, the premise. (See gold_label.)
sentence2: A string containing one sentence in Spanish, the hypothesis. (See gold_label.)
### Data Splits
The whole dataset was used for training. We did not use an evaluation split as we used the SemEval-2015 Task 2.
## Dataset Creation
### Curation Rationale
This corpus was built to remedy the scarcity of annotated Spanish-language datasets for NLI. It was generated by translating from the SNLI original dataset to Spanish using Argos. While machine translation is far from an ideal source for semantic classification, it is an aid to enlarging the data available.
### Source Data
#### Initial Data Collection and Normalization
Please refer to the respective documentations of the original datasets:
https://nlp.stanford.edu/projects/snli/
https://arxiv.org/pdf/1809.05053.pdf
https://cims.nyu.edu/~sbowman/multinli/
#### Who are the source language producers?
Please refer to the respective documentations of the original datasets:
https://nlp.stanford.edu/projects/snli/
https://arxiv.org/pdf/1809.05053.pdf
https://cims.nyu.edu/~sbowman/multinli/
### Annotations
#### Annotation process
Please refer to the respective documentations of the original datasets:
https://nlp.stanford.edu/projects/snli/
https://arxiv.org/pdf/1809.05053.pdf
https://cims.nyu.edu/~sbowman/multinli/
#### Who are the annotators?
Please refer to the respective documentations of the original datasets:
https://nlp.stanford.edu/projects/snli/
https://arxiv.org/pdf/1809.05053.pdf
https://cims.nyu.edu/~sbowman/multinli/
### Personal and Sensitive Information
In general, no sensitive information is conveyed in the sentences.
Please refer to the respective documentations of the original datasets:
https://nlp.stanford.edu/projects/snli/
https://arxiv.org/pdf/1809.05053.pdf
https://cims.nyu.edu/~sbowman/multinli/
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to offer new tools for semantic textual similarity analysis of Spanish sentences.
### Discussion of Biases
Please refer to the respective documentations of the original datasets:
https://nlp.stanford.edu/projects/snli/
https://arxiv.org/pdf/1809.05053.pdf
https://cims.nyu.edu/~sbowman/multinli/
### Other Known Limitations
The translation of the sentences was mostly unsupervised and may introduce some noise in the corpus. Machine translation from an English-language corpus is likely to generate syntactic and lexical forms that differ from those a human Spanish speaker would produce.
For discussion on the biases and limitations of the original datasets, please refer to their respective documentations:
https://nlp.stanford.edu/projects/snli/
https://arxiv.org/pdf/1809.05053.pdf
https://cims.nyu.edu/~sbowman/multinli/
## Additional Information
### Dataset Curators
The nli-es dataset was put together by Anibal Pérez, Lautaro Gesuelli, Mauricio Mazuecos and Emilio Tomás Ariza.
### Licensing Information
This corpus is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0).
Please refer to the respective documentations of the original datasets for information on their licenses:
https://nlp.stanford.edu/projects/snli/
https://arxiv.org/pdf/1809.05053.pdf
https://cims.nyu.edu/~sbowman/multinli/
### Citation Information
If you need to cite this dataset, you can link to this readme. | [
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sichenzhong/squad_v2_word2vec_aug | sichenzhong | 2022-03-30T00:45:41Z | 14 | 0 | null | [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sichenzhong/squad_v2_back_trans_synonym_aug | sichenzhong | 2022-03-30T18:49:26Z | 14 | 0 | null | [
"region:us"
] | 2022-03-30T18:49:26Z | 2022-03-30T18:48:20.000Z | 2022-03-30T18:48:20 | Entry not found | [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
copenlu/sufficient_facts | copenlu | 2022-08-05T08:33:48Z | 14 | 3 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|fever",
"source_datasets:extended|hover",
"source_datasets:extended|fever_gold_... | 2022-08-05T08:33:48Z | 2022-03-30T19:12:14.000Z | 2022-03-30T19:12:14 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: sufficient_facts
size_categories:
- 1K<n<10K
source_datasets:
- extended|fever
- extended|hover
- extended|fever_gold_evidence
task_categories:
- text-classification
task_ids:
- fact-checking
---
# Dataset Card for sufficient_facts
## Table of Contents
- [Table of Contents](#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:** https://github.com/copenlu/sufficient_facts
- **Repository:** https://github.com/copenlu/sufficient_facts
- **Paper:** Will be uploaded soon...
- **Leaderboard:**
- **Point of Contact:** https://apepa.github.io/
### Dataset Summary
This is the dataset SufficientFacts, introduced in the paper "Fact Checking with Insufficient Evidence", accepted at the TACL journal in 2022.
Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, **SufficientFacts**, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21% accuracy), whereas it is easiest for omitted date modifiers (63% accuracy). Finally, we propose a novel data augmentation strategy for contrastive self-learning of missing evidence by employing the proposed omission method combined with tri-training. It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1 score, which in turn improves FC performance by up to 2.6 F1 score.
### Languages
English
## Dataset Structure
The dataset consists of three files, each for one of the datasets -- FEVER, HoVer, and VitaminC.
Each file consists of json lines of the format:
```json
{
"claim": "Unison (Celine Dion album) was originally released by Atlantic Records.",
"evidence": [
[
"Unison (Celine Dion album)",
"The album was originally released on 2 April 1990 ."
]
],
"label_before": "REFUTES",
"label_after": "NOT ENOUGH",
"agreement": "agree_ei",
"type": "PP",
"removed": ["by Columbia Records"],
"text_orig": "[[Unison (Celine Dion album)]] The album was originally released on 2 April 1990 <span style=\"color:red;\">by Columbia Records</span> ."
}
```
### Data Instances
* FEVER: 600 consituent-level, 400 sentence-level;
* HoVer - 600 consituent-level, 400 sentence-level;
* VitaminC - 600 consituent-level.
### Data Fields
* `claim` - the claim that is being verified
* `evidence` - the augmented evidence for the claim, i.e. the evidence with some removed information
* `label_before` - the original label for the claim-evidence pair, before information was removed from the evidence
* `label_after` - the label for the augmented claim-evidence pair, after information was removed from the evidence, as annotated by crowd-source workers
* `type` - type of the information removed from the evidence. The types are fine-grained and their mapping to the general types -- 7 constituent and 1 sentence type can be found in [types.json](types.json) file.
* `removed` - the text of the removed information from the evidence
* `text_orig` - the original text of the evidence, as presented to crowd-source workers, the text of the removed information is inside `<span style=\"color:red;\"></span>` tags.
### Data Splits
| name |test_fever|test_hover|test_vitaminc|
|----------|-------:|-----:|-------:|
|test| 1000| 1000| 600|
Augmented from the test splits of the corresponding datasets.
### Annotations
#### Annotation process
The workers were provided with the following task description:
For each evidence text, some facts have been removed (marked in <span style="color:red;">red</span>).
You should annotate whether, <b>given the remaining facts in the evidence text, the evidence is still enough for verifying the claim.</b> <br></br>
<ul>
<li>You should select <i><b>'ENOUGH -- IRRELEVANT'</b></i>, if the <b>remaining information is still <i>enough</i></b> for verifying the claim because the <b>removed information is irrelevant</b> for identifying the evidence as SUPPORTS or REFUTES. See examples 1 and 2.</li>
<li>You should select <i><b>'ENOUGH -- REPEATED'</b></i>, if the <b>remaining information is still <i>enough</i></b> for verifying the claim because the <b>removed information is relevant but is also present (repeated) in the remaining (not red) text.</b> See example 3.</li>
<li>You should select <i><b>'NOT ENOUGH'</b></i> -- when <b>1) the removed information is <i>relevant</i></b> for verifying the claim <b> AND 2) it is <i>not present (repeated)</i> in the remaining text.</b> See examples 4, 5, and 6.</li>
<!--<li>You should select <i><b>'CHANGED INFO'</b></i> in the rare cases when the remaining evidence has <b>changed the support for the claim</b></li>-->
</ul>
<b>Note: You should not incorporate your own knowledge or beliefs! You should rely only on the evidence provided for the claim.</b>
The annotators were then given example instance annotations.
Finally, annotators were asked to complete a qualification test in order to be allowed to annotate instances for the task.
The resulting inter-annotator agreement for SufficientFacts is 0.81 Fleiss'k from three annotators.
#### Who are the annotators?
The annotations were performed by workers at Amazon Mechanical Turk.
## Additional Information
### Licensing Information
MIT
### Citation Information
```
@article{10.1162/tacl_a_00486,
author = {Atanasova, Pepa and Simonsen, Jakob Grue and Lioma, Christina and Augenstein, Isabelle},
title = "{Fact Checking with Insufficient Evidence}",
journal = {Transactions of the Association for Computational Linguistics},
volume = {10},
pages = {746-763},
year = {2022},
month = {07},
abstract = "{Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, SufficientFacts1, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21\\% accuracy), whereas it is easiest for omitted date modifiers (63\\% accuracy). Finally, we propose a novel data augmentation strategy for contrastive self-learning of missing evidence by employing the proposed omission method combined with tri-training. It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1 score, which in turn improves FC performance by up to 2.6 F1 score.}",
issn = {2307-387X},
doi = {10.1162/tacl_a_00486},
url = {https://doi.org/10.1162/tacl\_a\_00486},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00486/2037141/tacl\_a\_00486.pdf},
}
```
### Contributions
Thanks to [@apepa](https://github.com/apepa) for adding this dataset. | [
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hackathon-pln-es/disco_spanish_poetry | hackathon-pln-es | 2022-03-30T21:50:28Z | 14 | 8 | null | [
"region:us"
] | 2022-03-30T21:50:28Z | 2022-03-30T21:47:36.000Z | 2022-03-30T21:47:36 | # DISCO: Diachronic Spanish Sonnet Corpus
[](https://zenodo.org/badge/latestdoi/103841064)
The Diachronic Spanish Sonnet Corpus (DISCO) contains sonnets in Spanish in CSV, between the 15th and the 20th centuries (4303 sonnets by 1215 authors from 22 different countries). It includes well-known authors, but also less canonized ones.
This is a CSV compilation taken from the plain text corpus v4 published on git https://github.com/pruizf/disco/tree/v4. It includes the title, author, age and text metadata.
<br><br> | [
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nateraw/test-imagefolder-dataset | nateraw | 2022-03-30T22:19:04Z | 14 | 0 | null | [
"region:us"
] | 2022-03-30T22:19:04Z | 2022-03-30T21:58:59.000Z | 2022-03-30T21:58:59 | # test-imagefolder-dataset
This dataset shows that you can upload image folders (with an accompanying info.csv file within) to share and visualize multiple splits of a dataset. Cheers 🍻 | [
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DioLiu/Test2 | DioLiu | 2022-03-31T04:29:27Z | 14 | 0 | null | [
"region:us"
] | 2022-03-31T04:29:27Z | 2022-03-31T04:27:25.000Z | 2022-03-31T04:27:25 | Entry not found | [
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Splend1dchan/phone-squad-parquet | Splend1dchan | 2022-03-31T13:33:03Z | 14 | 0 | null | [
"region:us"
] | 2022-03-31T13:33:03Z | 2022-03-31T12:03:32.000Z | 2022-03-31T12:03:32 | Entry not found | [
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0.5715675354003906,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
LeoFeng/MLHW_6 | LeoFeng | 2022-03-31T12:35:46Z | 14 | 0 | null | [
"license:afl-3.0",
"region:us"
] | 2022-03-31T12:35:46Z | 2022-03-31T12:26:38.000Z | 2022-03-31T12:26:38 | ---
license: afl-3.0
---
| [
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-0.047826044261455536,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Samip/Scotch | Samip | 2022-04-29T14:19:23Z | 14 | 0 | null | [
"region:us"
] | 2022-04-29T14:19:23Z | 2022-03-31T12:31:51.000Z | 2022-03-31T12:31:51 |
## Dataset Summary
Scotch is a dataset of about 19 million functions collected from open-source repositiories from GitHub with permissive licenses. Each function has its corresponding code context and about 4 million functions have corresponding docstrings.
### Languages
The dataset includes functions written in programming languages Python, Java, Javascript, and Go.
## Statistics
### Split
The functions with docstrings is splitted into train, valid, and test set of 3200626, 400077, 400080 functions respectively.
## Features
Each function consists of following features:
* repository_name: Name of the repository the function belongs to.
* function_path: Path of the function within the repository.
* function_identifier: Function name/identifier.
* language: Programming language the function is written in.
* function: Function string.
* docstring: Function docstring.
* function_url: URL to the function code.
* context: Code context.
* license: License info of the repository (includes only repositories with permissive licenses).
## Data Collection
The dataset is collected from GitHub repositories of respective languages with 5 or more stars. Such repositories are listed using [SEART](https://seart-ghs.si.usi.ch/). Functions are parsed using a lightweight parser build on top of function parser from [CodeSearchNet dataset](https://github.com/github/CodeSearchNet/tree/master/function_parser) and repositories were collected with help of [github-downloader from EleutherAI](https://github.com/EleutherAI/github-downloader).
### Data Processing
All the code without permissive licenses are removed and deduplication is performed on the remaining set of functions. Afterwards, all the functions with single line of code, whose docstring contains non-English characters are removed. Files with multiple same functions are excluded. This results in about 19M functions. To obtain a dataset of NL-Code pairs, functions with no docstrings or doctrings less than 3 tokens separated by white-space are excluded. Following CodeSearchNet, functions with 'test' keyword in their name are excluded.
## License
This dataset is under MIT License. However, the repositories the functions are collected from may have several permissive licenses. Those licenses include MIT License, Apache License 2.0, BSD 3-Clause “New” or “Revised” License, BSD 2-Clause “Simplified” License, and ISC License. | [
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rubrix/news | rubrix | 2022-03-31T15:31:09Z | 14 | 0 | null | [
"region:us"
] | 2022-03-31T15:31:09Z | 2022-03-31T15:31:02.000Z | 2022-03-31T15:31:02 | Entry not found | [
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rubrix/news_test | rubrix | 2022-03-31T15:32:10Z | 14 | 0 | null | [
"region:us"
] | 2022-03-31T15:32:10Z | 2022-03-31T15:32:04.000Z | 2022-03-31T15:32:04 | Entry not found | [
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rubrix/test_datasetdict | rubrix | 2022-03-31T15:34:38Z | 14 | 0 | null | [
"region:us"
] | 2022-03-31T15:34:38Z | 2022-03-31T15:34:27.000Z | 2022-03-31T15:34:27 | Entry not found | [
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johnowhitaker/glid3_orbs | johnowhitaker | 2022-04-01T03:58:57Z | 14 | 0 | null | [
"region:us"
] | 2022-04-01T03:58:57Z | 2022-03-31T15:46:41.000Z | 2022-03-31T15:46:41 | These orbs were generated with GLID-3, a text-to-image system (https://github.com/Jack000/glid-3)
The text prompt for many was "Orbs within orbs, concentric circles and ripples of fire (spheres and circles, roundness)"
I used a high guidance scale (10 IIRC) and generated them in batches of 64
There are two 'flavours', 'dark' and 'light' (indicated with the 'label' attribute in the dataset. The 'light' images are from a GLID-3 model I fine-tuned on some abstract art, and tend to be more pastel colors and plain shapes. The 'dark' images are from GLID-3 part way through it's training.
This dataset is intended for use in GAN training demos and other art projects. Please give attribution if you use it in your own work (and tag me @johnowhitaker so I can see what you make!)
It's also nice for other artsy things, such as this montage made up of many little orb images: https://www.easyzoom.com/imageaccess/47cab299796a45edbd98951e704cb340
gan trained on this dataset: https://huggingface.co/johnowhitaker/orbgan_e1
gan demo (spaces): https://huggingface.co/spaces/johnowhitaker/orbgan_demo | [
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nntadotzips/vietjack_geography_original_labeled | nntadotzips | 2022-04-01T17:30:16Z | 14 | 0 | null | [
"region:us"
] | 2022-04-01T17:30:16Z | 2022-03-31T15:50:09.000Z | 2022-03-31T15:50:09 | Entry not found | [
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sichenzhong/squad_v2_back_trans_possib_aug | sichenzhong | 2022-04-01T03:18:19Z | 14 | 0 | null | [
"region:us"
] | 2022-04-01T03:18:19Z | 2022-04-01T03:11:25.000Z | 2022-04-01T03:11:25 | Entry not found | [
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0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sichenzhong/squad_v2_back_trans_synonym_possib_aug | sichenzhong | 2022-04-01T03:15:39Z | 14 | 0 | null | [
"region:us"
] | 2022-04-01T03:15:39Z | 2022-04-01T03:13:53.000Z | 2022-04-01T03:13:53 | Entry not found | [
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0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Ericblancosf/subtechnique | Ericblancosf | 2022-04-01T05:02:50Z | 14 | 0 | null | [
"region:us"
] | 2022-04-01T05:02:50Z | 2022-04-01T04:56:18.000Z | 2022-04-01T04:56:18 | Mitre technique subtechnqie | [
-0.47447794675827026,
-0.7313956022262573,
-0.04882677644491196,
0.6606531739234924,
-0.5365181565284729,
0.24851831793785095,
0.4431276321411133,
0.3407638669013977,
0.7575463056564331,
0.7070347666740417,
-0.6400747299194336,
-0.2781645655632019,
-0.4222099184989929,
-0.46954622864723206... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
HosseinGT/wider_face_background | HosseinGT | 2022-04-01T09:51:07Z | 14 | 0 | null | [
"region:us"
] | 2022-04-01T09:51:07Z | 2022-04-01T09:37:08.000Z | 2022-04-01T09:37:08 | Entry not found | [
-0.32276496291160583,
-0.22568435966968536,
0.8622260093688965,
0.43461480736732483,
-0.5282987952232361,
0.7012965083122253,
0.7915714979171753,
0.07618625462055206,
0.7746025323867798,
0.25632181763648987,
-0.7852815389633179,
-0.22573819756507874,
-0.9104480743408203,
0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sfdkiaei/EAS | sfdkiaei | 2022-04-01T11:14:44Z | 14 | 0 | null | [
"region:us"
] | 2022-04-01T11:14:44Z | 2022-04-01T10:46:59.000Z | 2022-04-01T10:46:59 | # EAS Dataset
[](https://opendatacommons.org/licenses/odbl/)
Emotions Analytic System (EAS) on Instagram social network data
Nowadays, thanks to spread of social media, and large amount of data in Internet, the need for changing how we look and interpret data is evolving. Visualization is one of the most important fields in data science. About growing usage of social media, analyzing the data they contain is crucial. In this research, the Emotion Analytic System on Instagram social network data designed and developed. In this system, we analyze emotions and words that user writes, and visualize them by visualizing techniques. Over 370,000 Instagram comments have been collected with the help of data crawlers that we developed, after that we prepared the data and preprocessed them; including normalizing, finding the keywords and etc. The system is developed by Python.
This Dataset has over 370,000 preprocessed comments (that most of them are in Persian) from 40 instagram channels. These comments are crawled from 12 April 2017 (1396/01/26 A.H.S) to 29 July 2017 (1396/05/07 A.H.S).
# Citation
If you use this dataset in your publications, please cite this paper:
```
@article {
author = {Kiaei, Seyed Faridoddin and Dehghan Rouzi, Mohammad and Farzi, Saeed},
title = {Designing and Implementing an Emotion Analytic System (EAS) on Instagram Social Network Data},
journal = {International Journal of Web Research},
volume = {2},
number = {2},
pages = {9-14},
year = {2019},
publisher = {University of Science and Culture},
issn = {2645-4335},
eissn = {2645-4343},
doi = {10.22133/ijwr.2020.225574.1052},
keywords = {Emotion Analysis,visualization,Instagram,Election},
url = {http://ijwr.usc.ac.ir/article_110287.html},
eprint = {http://ijwr.usc.ac.ir/article_110287_ad2b34be8792fd3e55ae13ea0f367b7a.pdf}
}
```
| [
-0.6436206102371216,
-0.48113328218460083,
0.4607219696044922,
0.6399085521697998,
-0.5224238038063049,
0.21824030578136444,
-0.14694702625274658,
-0.42753419280052185,
0.5213121175765991,
0.0878191888332367,
-0.6719641089439392,
-0.9839112162590027,
-0.534393310546875,
0.34337425231933594... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/few-shot-grumpy-cat | huggan | 2022-04-12T14:05:58Z | 14 | 0 | null | [
"arxiv:2101.04775",
"region:us"
] | 2022-04-12T14:05:58Z | 2022-04-01T11:36:28.000Z | 2022-04-01T11:36:28 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
-0.5524431467056274,
-0.8028349280357361,
0.018525345250964165,
0.33572760224342346,
-0.09379876405000687,
-0.17921070754528046,
-0.08067688345909119,
-0.28826087713241577,
0.07932981103658676,
-0.041977155953645706,
-0.35484322905540466,
-0.3427698016166687,
-0.3939037024974823,
0.0571840... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/few-shot-cat | huggan | 2022-04-12T14:06:50Z | 14 | 1 | null | [
"arxiv:2101.04775",
"region:us"
] | 2022-04-12T14:06:50Z | 2022-04-01T11:40:37.000Z | 2022-04-01T11:40:37 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
-0.5524431467056274,
-0.8028349280357361,
0.018525345250964165,
0.33572760224342346,
-0.09379876405000687,
-0.17921070754528046,
-0.08067688345909119,
-0.28826087713241577,
0.07932981103658676,
-0.041977155953645706,
-0.35484322905540466,
-0.3427698016166687,
-0.3939037024974823,
0.0571840... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
blo05/cleaned_wiki_en_40-60 | blo05 | 2022-04-01T11:54:18Z | 14 | 0 | null | [
"region:us"
] | 2022-04-01T11:54:18Z | 2022-04-01T11:45:40.000Z | 2022-04-01T11:45:40 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
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-0.52829909324646,
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0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104477167129517,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/few-shot-fauvism-still-life | huggan | 2022-04-12T14:07:31Z | 14 | 0 | null | [
"arxiv:2101.04775",
"region:us"
] | 2022-04-12T14:07:31Z | 2022-04-01T11:47:44.000Z | 2022-04-01T11:47:44 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
-0.5524430871009827,
-0.802834689617157,
0.01852492056787014,
0.3357279300689697,
-0.09379845857620239,
-0.17921093106269836,
-0.08067672699689865,
-0.28826087713241577,
0.07932962477207184,
-0.04197702184319496,
-0.3548423647880554,
-0.342769593000412,
-0.39390403032302856,
0.057183869183... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/few-shot-flat-colored-patterns | huggan | 2022-04-12T14:07:41Z | 14 | 1 | null | [
"arxiv:2101.04775",
"region:us"
] | 2022-04-12T14:07:41Z | 2022-04-01T11:54:39.000Z | 2022-04-01T11:54:39 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
-0.5524430871009827,
-0.802834689617157,
0.01852492056787014,
0.3357279300689697,
-0.09379845857620239,
-0.17921093106269836,
-0.08067672699689865,
-0.28826087713241577,
0.07932962477207184,
-0.04197702184319496,
-0.3548423647880554,
-0.342769593000412,
-0.39390403032302856,
0.057183869183... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/few-shot-moongate | huggan | 2022-04-12T14:07:11Z | 14 | 0 | null | [
"arxiv:2101.04775",
"region:us"
] | 2022-04-12T14:07:11Z | 2022-04-01T11:55:18.000Z | 2022-04-01T11:55:18 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
-0.5524430871009827,
-0.802834689617157,
0.01852492056787014,
0.3357279300689697,
-0.09379845857620239,
-0.17921093106269836,
-0.08067672699689865,
-0.28826087713241577,
0.07932962477207184,
-0.04197702184319496,
-0.3548423647880554,
-0.342769593000412,
-0.39390403032302856,
0.057183869183... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/few-shot-shells | huggan | 2022-04-12T14:07:59Z | 14 | 1 | null | [
"arxiv:2101.04775",
"region:us"
] | 2022-04-12T14:07:59Z | 2022-04-01T11:56:38.000Z | 2022-04-01T11:56:38 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
-0.5524430871009827,
-0.802834689617157,
0.01852492056787014,
0.3357279300689697,
-0.09379845857620239,
-0.17921093106269836,
-0.08067672699689865,
-0.28826087713241577,
0.07932962477207184,
-0.04197702184319496,
-0.3548423647880554,
-0.342769593000412,
-0.39390403032302856,
0.057183869183... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/few-shot-skulls | huggan | 2022-04-12T14:03:56Z | 14 | 0 | null | [
"arxiv:2101.04775",
"region:us"
] | 2022-04-12T14:03:56Z | 2022-04-01T11:57:06.000Z | 2022-04-01T11:57:06 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
-0.5524431467056274,
-0.8028349280357361,
0.018525348976254463,
0.33572760224342346,
-0.09379876405000687,
-0.17921070754528046,
-0.08067688345909119,
-0.28826087713241577,
0.07932981103658676,
-0.041977155953645706,
-0.3548431992530823,
-0.3427698016166687,
-0.3939037024974823,
0.05718407... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
PaulLerner/viquae_passages | PaulLerner | 2023-05-31T11:41:41Z | 14 | 0 | null | [
"region:us"
] | 2023-05-31T11:41:41Z | 2022-04-01T15:45:33.000Z | 2022-04-01T15:45:33 | Deprecated as of meerqat v4-alpha. See https://github.com/PaulLerner/ViQuAE | [
-0.4610525667667389,
-0.6307443976402283,
0.3666771650314331,
0.43075746297836304,
-0.5822009444236755,
0.0721326693892479,
0.5199441909790039,
-0.28685125708580017,
0.30147430300712585,
0.07214444130659103,
-0.6774353384971619,
-0.4845583736896515,
-0.31621256470680237,
0.0265035778284072... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hackathon-pln-es/biomed_squad_es_v2 | hackathon-pln-es | 2022-04-03T17:46:58Z | 14 | 2 | null | [
"arxiv:1912.05200",
"region:us"
] | 2022-04-03T17:46:58Z | 2022-04-02T03:05:44.000Z | 2022-04-02T03:05:44 | # Dataset Card for biomed_squad_es_v2
This Dataset was created as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
## 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 subset of the [dev squad_es (v2) dataset](https://huggingface.co/datasets/squad_es) (automatic translation of the Stanford Question Answering Dataset v2 into Spanish) containing questions related to the biomedical domain.
License, distribution and usage conditions of the original Squad_es Dataset apply.
### Languages
Spanish
## Dataset Structure
### Data Fields
```
{'answers': {'answer_start': [343, 343, 343],
'text': ['diez veces su propio peso',
'diez veces su propio peso',
'diez veces su propio peso']},
'context': 'Casi todos los ctenóforos son depredadores, tomando presas que van desde larvas microscópicas y rotíferos a los adultos de pequeños crustáceos; Las excepciones son los juveniles de dos especies, que viven como parásitos en las salpas en las que los adultos de su especie se alimentan. En circunstancias favorables, los ctenóforos pueden comer diez veces su propio peso en un día. Sólo 100-150 especies han sido validadas, y posiblemente otras 25 no han sido completamente descritas y nombradas. Los ejemplos de libros de texto son cidipidos con cuerpos en forma de huevo y un par de tentáculos retráctiles bordeados con tentilla ("pequeños tentáculos") que están cubiertos con colúnculos, células pegajosas. El filo tiene una amplia gama de formas corporales, incluyendo los platyctenidos de mar profundo, en los que los adultos de la mayoría de las especies carecen de peines, y los beroides costeros, que carecen de tentáculos. Estas variaciones permiten a las diferentes especies construir grandes poblaciones en la misma área, porque se especializan en diferentes tipos de presas, que capturan por una amplia gama de métodos que utilizan las arañas.',
'id': '5725c337271a42140099d165',
'question': '¿Cuánta comida come un Ctenophora en un día?',
'title': 'Ctenophora'}
```
### Data Splits
Validation: 1137 examples
### Citation Information
```
@article{2016arXiv160605250R,
author = {Casimiro Pio , Carrino and Marta R. , Costa-jussa and Jose A. R. , Fonollosa},
title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
Question Answering}",
journal = {arXiv e-prints},
year = 2019,
eid = {arXiv:1912.05200v1},
pages = {arXiv:1912.05200v1},
archivePrefix = {arXiv},
eprint = {1912.05200v2},
}
```
## Team
Santiago Maximo: [smaximo](https://huggingface.co/smaximo) | [
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-0.5733885169029236,
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0.2518770694732666,
-0.7830827832221985,
-0.7826175689697266,
-0.3750254511833191,
0.6683197617530... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hackathon-pln-es/spanish-poetry-dataset | hackathon-pln-es | 2022-04-03T03:34:26Z | 14 | 2 | null | [
"region:us"
] | 2022-04-03T03:34:26Z | 2022-04-03T03:31:57.000Z | 2022-04-03T03:31:57 | This dataset was previously created in Kaggle by [Andrea Morales Garzón](https://huggingface.co/andreamorgar).
[Link Kaggle](https://www.kaggle.com/andreamorgar/spanish-poetry-dataset/version/1) | [
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0.35186904668807983,
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0.9930437803268433,
0.9860139489173889,
-1.0529630184173584,
-0.780153214931488,
-0.6035382747650146,
-0.033891126513... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
abdulhady/ckb | abdulhady | 2022-04-03T10:52:39Z | 14 | 0 | null | [
"license:other",
"region:us"
] | 2022-04-03T10:52:39Z | 2022-04-03T10:49:55.000Z | 2022-04-03T10:49:55 | ---
license: other
---
| [
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blo05/cleaned_wiki_en_60-80 | blo05 | 2022-04-03T12:02:35Z | 14 | 0 | null | [
"region:us"
] | 2022-04-03T12:02:35Z | 2022-04-03T11:52:56.000Z | 2022-04-03T11:52:56 | Entry not found | [
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miracFence/scientific_papers_es | miracFence | 2022-04-03T23:57:12Z | 14 | 0 | null | [
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hackathon-pln-es/scientific_papers_en_es | hackathon-pln-es | 2022-04-03T23:59:39Z | 14 | 1 | null | [
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ikekobby/40-percent-cleaned-preprocessed-fake-real-news | ikekobby | 2022-04-04T09:41:40Z | 14 | 0 | null | [
"region:us"
] | 2022-04-04T09:41:40Z | 2022-04-04T09:26:47.000Z | 2022-04-04T09:26:47 | Kaggle based dataset for text classification task. The data has been cleaned and processed for preparation into any model for classification based tasks. This is just 40% of the entire dataset. | [
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huggan/inat_butterflies | huggan | 2022-04-04T10:53:19Z | 14 | 1 | null | [
"region:us"
] | 2022-04-04T10:53:19Z | 2022-04-04T10:34:36.000Z | 2022-04-04T10:34:36 | This dataset contains images from iNaturalist of butterflies (superfamily Papilionoidea) with at least one fave. Check the descriptions - some images have a licence like CC-BY-NC and can't be used for commercial purposes.
The list of observations was exported from iNaturalist after a query similar to https://www.inaturalist.org/observations?place_id=any&popular&taxon_id=47224
The images were downloaded with img2dataset and uploaded to the huggingface hub by @johnowhitaker using this colab notebook: https://colab.research.google.com/drive/14qwFV_G4dh6evizzqHP08qDUAHtzfuiW?usp=sharing
The goal is to have a dataset of butterflies in different poses and settings, to use for GAN training and to compare with datasets built with museum collections of pinned specimens (which tend to be much cleaner and have more consistency of pose etc)
I'm not familiar with the nuances of creative commons licencing but you may wish to filter out images which are no-derivatices (CC-...-ND) when training a GAN or creating new images. | [
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Nart/abkhaz_text | Nart | 2022-11-01T10:53:17Z | 14 | 3 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
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"language:ab",
"license:cc0-1.0",
"region:us"
] | 2022-11-01T10:53:17Z | 2022-04-04T11:57:51.000Z | 2022-04-04T11:57:51 | ---
language_creators:
- expert-generated
language:
- ab
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Abkhaz monolingual corpus
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for "Abkhaz text"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
## Dataset Description
- **Point of Contact:** [Nart Tlisha](mailto:daniel.abzakh@gmail.com)
- **Size of the generated dataset:** 176 MB
### Dataset Summary
The Abkhaz language monolingual dataset is a collection of 1,470,480 sentences extracted from different sources. The dataset is available under the Creative Commons Universal Public Domain License. Part of it is also available as part of [Common Voice](https://commonvoice.mozilla.org/ab), another part is from the [Abkhaz National Corpus](https://clarino.uib.no/abnc)
## Dataset Creation
### Source Data
Here is a link to the source of a large part of the data on [github](https://github.com/danielinux7/Multilingual-Parallel-Corpus/blob/master/ebooks/reference.md)
## Considerations for Using the Data
### Other Known Limitations
The accuracy of the dataset is around 95% (gramatical, arthographical errors)
| [
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lislia/clean_policyQA_train | lislia | 2022-04-04T12:05:47Z | 14 | 0 | null | [
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damlab/human_hiv_ppi | damlab | 2022-04-04T14:38:49Z | 14 | 0 | null | [
"license:mit",
"region:us"
] | 2022-04-04T14:38:49Z | 2022-04-04T14:24:30.000Z | 2022-04-04T14:24:30 | ---
license: mit
---
# Dataset Description
## Dataset Summary
This dataset was parsed from the Human-HIV Interaction dataset maintained by the NCBI.
It contains a >16,000 pairs of interactions between HIV and Human proteins.
Sequences of the interacting proteins were retrieved from the NCBI protein database and added to the dataset.
The raw data is available from the [NBCI FTP site](https://ftp.ncbi.nlm.nih.gov/gene/GeneRIF/hiv_interactions.gz) and the curation strategy is described in the [NAR Research paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383939/) announcing the dataset.
## Dataset Structure
### Data Instances
Data Fields: hiv_protein_product, hiv_protein_name, interaction_type, human_protein_product, human_protein_name, reference_list, description, hiv_protein_sequence, human_protein_sequence
Data Splits: None
## Dataset Creation
Curation Rationale: This dataset was curated train models to recognize proteins that interact with HIV.
Initial Data Collection and Normalization: Dataset was downloaded and curated on 4/4/2022 but the most recent update of the underlying NCBI database was 2016.
## Considerations for Using the Data
Discussion of Biases: This dataset of protein interactions was manually curated by experts utilizing published scientific literature.
This inherently biases the collection to well-studied proteins and known interactions.
The dataset does not contain _negative_ interactions.
## Additional Information:
- Dataset Curators: Will Dampier
- Citation Information: TBA | [
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met/mm | met | 2022-04-04T18:42:01Z | 14 | 0 | null | [
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"region:us"
] | 2022-04-04T18:42:01Z | 2022-04-04T18:39:59.000Z | 2022-04-04T18:39:59 | ---
license: apache-2.0
---
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huggan/smithsonian-butterfly-lowres | huggan | 2022-04-06T19:57:24Z | 14 | 3 | null | [
"license:cc0-1.0",
"region:us"
] | 2022-04-06T19:57:24Z | 2022-04-04T18:45:28.000Z | 2022-04-04T18:45:28 | ---
license: cc0-1.0
---
Collection of pinned butterfly images from the Smithsonian https://www.si.edu/spotlight/buginfo/butterfly
Doesn't include metadata yet!
Url pattern: "https://ids.si.edu/ids/deliveryService?max_w=550&id=ark:/65665/m3c70e17cf30314fd4ad86afa7d1ebf49f"
Added sketch versions!
sketch_pidinet is generated by : https://github.com/zhuoinoulu/pidinet
sketch_pix2pix is generated by : https://github.com/mtli/PhotoSketch
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met/Meti_ICT | met | 2022-04-05T11:56:09Z | 14 | 0 | null | [
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license: ms-pl
---
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DioLiu/Test3 | DioLiu | 2022-04-09T04:05:30Z | 14 | 0 | null | [
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rafay/upside_down_detection_cifar100 | rafay | 2022-04-05T06:51:09Z | 14 | 0 | null | [
"license:afl-3.0",
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] | 2022-04-05T06:51:09Z | 2022-04-05T06:43:32.000Z | 2022-04-05T06:43:32 | ---
license: afl-3.0
---
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aaraki/github-issues2 | aaraki | 2022-04-21T07:42:02Z | 14 | 0 | null | [
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met/AMH_MET | met | 2022-04-05T11:46:16Z | 14 | 0 | null | [
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] | 2022-04-05T11:46:16Z | 2022-04-05T11:44:56.000Z | 2022-04-05T11:44:56 | ---
license: apache-2.0
---
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license: apache-2.0
---
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NLPC-UOM/Sinhala-News-Source-classification | NLPC-UOM | 2022-10-25T10:04:01Z | 14 | 0 | null | [
"task_categories:text-classification",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:si",
"license:mit",
"region:us"
] | 2022-10-25T10:04:01Z | 2022-04-07T12:43:58.000Z | 2022-04-07T12:43:58 | ---
annotations_creators: []
language_creators:
- crowdsourced
language:
- si
license:
- mit
multilinguality:
- monolingual
pretty_name: sinhala-news-source-classification
size_categories: []
source_datasets: []
task_categories:
- text-classification
task_ids: []
---
This dataset contains Sinhala news headlines extracted from 9 news sources (websites) (Sri Lanka Army, Dinamina, GossipLanka, Hiru, ITN, Lankapuwath, NewsLK,
Newsfirst, World Socialist Web Site-Sinhala). This is a processed version of the corpus created by *Sachintha, D., Piyarathna, L., Rajitha, C., and Ranathunga, S. (2021). Exploiting parallel corpora to improve multilingual embedding based document and sentence alignment*. Single word sentences, invalid characters have been removed from the originally extracted corpus and also subsampled to handle class imbalance.
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*} | [
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GEM-submissions/ratishsp__ent__1649421332 | GEM-submissions | 2022-04-08T12:35:35Z | 14 | 0 | null | [
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"benchmark",
"region:us"
] | 2022-04-08T12:35:35Z | 2022-04-08T12:35:32.000Z | 2022-04-08T12:35:32 | ---
benchmark: gem
type: prediction
submission_name: ENT
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: ENT
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GEM-submissions/ratishsp__ncp_cc__1649422112 | GEM-submissions | 2022-04-08T12:48:34Z | 14 | 0 | null | [
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] | 2022-04-08T12:48:34Z | 2022-04-08T12:48:32.000Z | 2022-04-08T12:48:32 | ---
benchmark: gem
type: prediction
submission_name: NCP_CC
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: NCP_CC
| [
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0.0098427431657910... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM-submissions/ratishsp__ent__1649422569 | GEM-submissions | 2022-04-08T12:56:11Z | 14 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-04-08T12:56:11Z | 2022-04-08T12:56:09.000Z | 2022-04-08T12:56:09 | ---
benchmark: gem
type: prediction
submission_name: ENT
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: ENT
| [
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GEM-submissions/ratishsp__ncp_cc__1649422863 | GEM-submissions | 2022-04-08T13:01:05Z | 14 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-04-08T13:01:05Z | 2022-04-08T13:01:03.000Z | 2022-04-08T13:01:03 | ---
benchmark: gem
type: prediction
submission_name: NCP_CC
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: NCP_CC
| [
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0.0098427431657910... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
juancopi81/github-issues | juancopi81 | 2022-04-08T14:24:36Z | 14 | 0 | null | [
"region:us"
] | 2022-04-08T14:24:36Z | 2022-04-08T14:21:26.000Z | 2022-04-08T14:21:26 | Entry not found | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yogi/amazon | yogi | 2022-04-11T09:10:14Z | 14 | 0 | null | [
"region:us"
] | 2022-04-11T09:10:14Z | 2022-04-11T09:06:48.000Z | 2022-04-11T09:06:48 | Entry not found | [
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-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
UrukHan/wav2vec2-ru-I | UrukHan | 2022-04-11T18:36:13Z | 14 | 0 | null | [
"region:us"
] | 2022-04-11T18:36:13Z | 2022-04-11T18:26:57.000Z | 2022-04-11T18:26:57 | Entry not found | [
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-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arakesh/test9920 | arakesh | 2022-04-12T10:24:49Z | 14 | 0 | null | [
"region:us"
] | 2022-04-12T10:24:49Z | 2022-04-12T10:01:46.000Z | 2022-04-12T10:01:46 | Entry not found | [
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-0.7852816581726074,
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-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yarongef/human_proteome_doublets | yarongef | 2022-09-21T08:43:43Z | 14 | 0 | null | [
"license:mit",
"region:us"
] | 2022-09-21T08:43:43Z | 2022-04-12T10:42:22.000Z | 2022-04-12T10:42:22 | ---
license: mit
---
# Dataset Description
Out of **20,577** human proteins (from [UniProt human proteome](https://www.uniprot.org/proteomes/UP000005640)), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of **12,703** proteins. The uShuffle algorithm ([python pacakge](https://github.com/guma44/ushuffle)) was then used to shuffle these protein sequences while maintaining their doublet distribution. The very few sequences for which uShuffle failed to create a shuffled version were eliminated.
Afterwards, h-CD-HIT algorithm ([web server](http://weizhong-lab.ucsd.edu/cdhit-web-server/cgi-bin/index.cgi)) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of **11,658** sequences.
# Citation
If you use this dataset, please cite our paper:
```
@article {
author = {Geffen, Yaron and Ofran, Yanay and Unger, Ron},
title = {DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts},
year = {2022},
doi = {10.1093/bioinformatics/btac474},
URL = {https://doi.org/10.1093/bioinformatics/btac474},
journal = {Bioinformatics}
}
``` | [
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-0.6138734817504883,
0.48791369795799255... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yarongef/human_proteome_triplets | yarongef | 2022-09-21T08:44:27Z | 14 | 0 | null | [
"license:mit",
"region:us"
] | 2022-09-21T08:44:27Z | 2022-04-12T10:44:30.000Z | 2022-04-12T10:44:30 | ---
license: mit
---
# Dataset Description
Out of **20,577** human proteins (from [UniProt human proteome](https://www.uniprot.org/proteomes/UP000005640)), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of **12,703** proteins. The uShuffle algorithm ([python pacakge](https://github.com/guma44/ushuffle)) was then used to shuffle these protein sequences while maintaining their triplet distribution. The sequences for which uShuffle failed to create a shuffled version were eliminated.
Afterwards, h-CD-HIT algorithm ([web server](http://weizhong-lab.ucsd.edu/cdhit-web-server/cgi-bin/index.cgi)) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of **3,688** sequences.
# Citation
If you use this dataset, please cite our paper:
```
@article {
author = {Geffen, Yaron and Ofran, Yanay and Unger, Ron},
title = {DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts},
year = {2022},
doi = {10.1093/bioinformatics/btac474},
URL = {https://doi.org/10.1093/bioinformatics/btac474},
journal = {Bioinformatics}
}
``` | [
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-0.5786803364753723,
0.549274504184722... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arakesh/PennFudanPedestrian-1024x512 | arakesh | 2022-04-12T16:14:33Z | 14 | 0 | null | [
"region:us"
] | 2022-04-12T16:14:33Z | 2022-04-12T15:58:09.000Z | 2022-04-12T15:58:09 | | images | semantic maps | instance ids |
| --- | --- | --- |
| available | available | available |
```
dataset-size: 107Mb
resolution: 1024x1024
license: ...
sample-size:
./pix2pixHD_person_synthesis
├── test_img [10 entries]
├── test_inst [10 entries]
├── test_label [10 entries]
├── train_img [160 entries]
├── train_inst [160 entries]
└── train_label [160 entries]
``` | [
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-0.6664507389068604,
0.1752324253320694,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arakesh/deepglobe-2448x2448 | arakesh | 2022-04-12T17:20:26Z | 14 | 1 | null | [
"region:us"
] | 2022-04-12T17:20:26Z | 2022-04-12T16:20:33.000Z | 2022-04-12T16:20:33 | Data source: http://deepglobe.org/
| images | semantic maps | instance ids |
| --- | --- | --- |
| available | available | n/a |
```
dataset-size: 2.0G
resolution: 2448x2448
license: ...
sample-size:
./pix2pixHD-deepglobe-synthesis
├── test_img [30 entries]
├── test_label [30 entries]
├── train_img [773 entries]
└── train_label [773 entries]
``` | [
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-0.6810057759284973,
-0.1689740121364... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stevhliu/dummy | stevhliu | 2023-05-03T18:14:35Z | 14 | 0 | null | [
"region:us"
] | 2023-05-03T18:14:35Z | 2022-04-18T17:01:14.000Z | 2022-04-18T17:01:14 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
h4iku/coconut_c2005_preprocessed | h4iku | 2022-04-21T11:39:26Z | 14 | 0 | null | [
"region:us"
] | 2022-04-21T11:39:26Z | 2022-04-21T08:37:46.000Z | 2022-04-21T08:37:46 | Entry not found | [
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-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
eleldar/sub_train-normal_tests-datasets | eleldar | 2022-06-16T11:19:47Z | 14 | 0 | null | [
"region:us"
] | 2022-06-16T11:19:47Z | 2022-04-21T15:25:32.000Z | 2022-04-21T15:25:32 | Dataset for API: https://github.com/eleldar/Translation
Test English-Russian dataset:
```
DatasetDict({
normal: Dataset({
features: ['en', 'ru'],
num_rows: 2009
})
short: Dataset({
features: ['en', 'ru'],
num_rows: 2664
})
train: Dataset({
features: ['en', 'ru'],
num_rows: 1660
})
validation: Dataset({
features: ['en', 'ru'],
num_rows: 208
})
test: Dataset({
features: ['en', 'ru'],
num_rows: 4170
})
})
```
The dataset get from tables:
* https://github.com/eleldar/Translator/blob/master/test_dataset/flores101_dataset/101_languages.xlsx?raw=true
* https://github.com/eleldar/Translator/blob/master/test_dataset/normal.xlsx?raw=true
* https://github.com/eleldar/Translator/blob/master/test_dataset/corrected_vocab.xlsx?raw=true | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM-submissions/ratishsp__seqplan-sportsett__1650556902 | GEM-submissions | 2022-04-21T16:01:45Z | 14 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-04-21T16:01:45Z | 2022-04-21T16:01:43.000Z | 2022-04-21T16:01:43 | ---
benchmark: gem
type: prediction
submission_name: SeqPlan-SportSett
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: SeqPlan-SportSett
| [
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0.4356596767902374,
-1.0463999509811401,
-0.22837434709072113,
-0.505714476108551,
-0.09105850756168365,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pietrolesci/glue_diagnostics | pietrolesci | 2022-04-21T16:51:56Z | 14 | 0 | null | [
"region:us"
] | 2022-04-21T16:51:56Z | 2022-04-21T16:46:38.000Z | 2022-04-21T16:46:38 | ## Overview
Original dataset available [here](https://gluebenchmark.com/diagnostics).
## Dataset curation
Filled in the empty rows of columns "lexical semantics", "predicate-argument structure",
"logic", "knowledge" with empty string `""`.
Labels are encoded as follows
```
{"entailment": 0, "neutral": 1, "contradiction": 2}
```
## Code to create dataset
```python
import pandas as pd
from datasets import Features, Value, ClassLabel, Dataset
df = pd.read_csv("<path to file>/diagnostic-full.tsv", sep="\t")
# column names to lower
df.columns = df.columns.str.lower()
# fill na
assert df["label"].isna().sum() == 0
df = df.fillna("")
# encode labels
df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
# cast to dataset
features = Features({
"lexical semantics": Value(dtype="string", id=None),
"predicate-argument structure": Value(dtype="string", id=None),
"logic": Value(dtype="string", id=None),
"knowledge": Value(dtype="string", id=None),
"domain": Value(dtype="string", id=None),
"premise": Value(dtype="string", id=None),
"hypothesis": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
})
dataset = Dataset.from_pandas(df, features=features)
dataset.push_to_hub("glue_diagnostics", token="<token>", split="test")
```
| [
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0.38245123624801636,
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-0.5977047681808472,
0.1425696611404419... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
adithya7/xlel_wd_dictionary | adithya7 | 2022-07-01T17:30:21Z | 14 | 1 | null | [
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:af",
"language:ar",
"language:be",
"language:bg",
"language:bn",
"language:ca",
"language:cs",
"language:da",
"language:de",
"langu... | 2022-07-01T17:30:21Z | 2022-04-22T02:36:27.000Z | 2022-04-22T02:36:27 | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- ar
- be
- bg
- bn
- ca
- cs
- da
- de
- el
- en
- es
- fa
- fi
- fr
- he
- hi
- hu
- id
- it
- ja
- ko
- ml
- mr
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- si
- sk
- sl
- sr
- sv
- sw
- ta
- te
- th
- tr
- uk
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: XLEL-WD is a multilingual event linking dataset. This supplementary dataset
contains a dictionary of event items from Wikidata. The descriptions for Wikidata
event items are taken from the corresponding multilingual Wikipedia articles.
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories: []
task_ids: []
---
# Dataset Card for XLEL-WD-Dictionary
## 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://github.com/adithya7/xlel-wd>
- **Repository:** <https://github.com/adithya7/xlel-wd>
- **Paper:** <https://arxiv.org/abs/2204.06535>
- **Leaderboard:** N/A
- **Point of Contact:** Adithya Pratapa
### Dataset Summary
XLEL-WD is a multilingual event linking dataset. This supplementary dataset contains a dictionary of event items from Wikidata. The descriptions for Wikidata event items are taken from the corresponding multilingual Wikipedia articles.
### Supported Tasks and Leaderboards
This dictionary can be used as a part of the event linking task.
### Languages
This dataset contains text from 44 languages. The language names and their ISO 639-1 codes are listed below. For details on the dataset distribution for each language, refer to the original paper.
| Language | Code | Language | Code | Language | Code | Language | Code |
| -------- | ---- | -------- | ---- | -------- | ---- | -------- | ---- |
| Afrikaans | af | Arabic | ar | Belarusian | be | Bulgarian | bg |
| Bengali | bn | Catalan | ca | Czech | cs | Danish | da |
| German | de | Greek | el | English | en | Spanish | es |
| Persian | fa | Finnish | fi | French | fr | Hebrew | he |
| Hindi | hi | Hungarian | hu | Indonesian | id | Italian | it |
| Japanese | ja | Korean | ko | Malayalam | ml | Marathi | mr |
| Malay | ms | Dutch | nl | Norwegian | no | Polish | pl |
| Portuguese | pt | Romanian | ro | Russian | ru | Sinhala | si |
| Slovak | sk | Slovene | sl | Serbian | sr | Swedish | sv |
| Swahili | sw | Tamil | ta | Telugu | te | Thai | th |
| Turkish | tr | Ukrainian | uk | Vietnamese | vi | Chinese | zh |
## Dataset Structure
### Data Instances
Each instance in the `label_dict.jsonl` file follows the below template,
```json
{
"label_id": "830917",
"label_title": "2010 European Aquatics Championships",
"label_desc": "The 2010 European Aquatics Championships were held from 4–15 August 2010 in Budapest and Balatonfüred, Hungary. It was the fourth time that the city of Budapest hosts this event after 1926, 1958 and 2006. Events in swimming, diving, synchronised swimming (synchro) and open water swimming were scheduled.",
"label_lang": "en"
}
```
### Data Fields
| Field | Meaning |
| ----- | ------- |
| `label_id` | Wikidata ID |
| `label_title` | Title for the event, as collected from the corresponding Wikipedia article |
| `label_desc` | Description for the event, as collected from the corresponding Wikipedia article |
| `label_lang` | language used for the title and description |
### Data Splits
This dictionary has a single split, `dictionary`. It contains 10947 event items from Wikidata and a total of 114834 text descriptions collected from multilingual Wikipedia articles.
## Dataset Creation
### Curation Rationale
This datasets helps address the task of event linking. KB linking is extensively studied for entities, but its unclear if the same methodologies can be extended for linking mentions to events from KB. Event items are collected from Wikidata.
### Source Data
#### Initial Data Collection and Normalization
A Wikidata item is considered a potential event if it has spatial and temporal properties. The final event set is collected after post-processing for quality control.
#### Who are the source language producers?
The titles and descriptions for the events are written by Wikipedia contributors.
### Annotations
#### Annotation process
This dataset was automatically compiled from Wikidata. It was post-processed to improve data quality.
#### Who are the annotators?
Wikidata and Wikipedia contributors.
### 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
This dictionary primarily contains eventive nouns from Wikidata. It does not include other event items from Wikidata such as disease outbreak (Q3241045), military offensive (Q2001676), war (Q198), etc.,
## Additional Information
### Dataset Curators
The dataset was curated by Adithya Pratapa, Rishubh Gupta and Teruko Mitamura. The code for collecting the dataset is available at [Github:xlel-wd](https://github.com/adithya7/xlel-wd).
### Licensing Information
XLEL-WD dataset is released under [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```bib
@article{pratapa-etal-2022-multilingual,
title = {Multilingual Event Linking to Wikidata},
author = {Pratapa, Adithya and Gupta, Rishubh and Mitamura, Teruko},
publisher = {arXiv},
year = {2022},
url = {https://arxiv.org/abs/2204.06535},
}
```
### Contributions
Thanks to [@adithya7](https://github.com/adithya7) for adding this dataset.
| [
-0.6484071016311646,
-0.24012501537799835,
0.19112859666347504,
-0.07805710285902023,
-0.16613152623176575,
-0.10610045492649078,
-0.24868221580982208,
-0.7207148671150208,
0.4058854579925537,
0.02626875601708889,
-0.7423096895217896,
-0.8412436246871948,
-0.46523505449295044,
0.4398377537... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pietrolesci/scitail | pietrolesci | 2022-04-25T10:40:47Z | 14 | 0 | null | [
"region:us"
] | 2022-04-25T10:40:47Z | 2022-04-22T09:06:21.000Z | 2022-04-22T09:06:21 | ## Overview
Original dataset is available on the HuggingFace Hub [here](https://huggingface.co/datasets/scitail).
## Dataset curation
This is the same as the `snli_format` split of the SciTail dataset available on the HuggingFace Hub (i.e., same data, same splits, etc).
The only differences are the following:
- selecting only the columns `["sentence1", "sentence2", "gold_label", "label"]`
- renaming columns with the following mapping `{"sentence1": "premise", "sentence2": "hypothesis"}`
- creating a new column "label" from "gold_label" with the following mapping `{"entailment": "entailment", "neutral": "not_entailment"}`
- encoding labels with the following mapping `{"not_entailment": 0, "entailment": 1}`
Note that there are 10 overlapping instances (as found by merging on columns "label", "premise", and "hypothesis") between
`train` and `test` splits.
## Code to create the dataset
```python
from datasets import Features, Value, ClassLabel, Dataset, DatasetDict, load_dataset
# load datasets from the Hub
dd = load_dataset("scitail", "snli_format")
ds = {}
for name, df_ in dd.items():
df = df_.to_pandas()
# select important columns
df = df[["sentence1", "sentence2", "gold_label"]]
# rename columns
df = df.rename(columns={"sentence1": "premise", "sentence2": "hypothesis"})
# encode labels
df["label"] = df["gold_label"].map({"entailment": "entailment", "neutral": "not_entailment"})
df["label"] = df["label"].map({"not_entailment": 0, "entailment": 1})
# cast to dataset
features = Features({
"premise": Value(dtype="string", id=None),
"hypothesis": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=2, names=["not_entailment", "entailment"]),
})
ds[name] = Dataset.from_pandas(df, features=features)
dataset = DatasetDict(ds)
dataset.push_to_hub("scitail", token="<token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(dataset.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
dataset[i].to_pandas(),
dataset[j].to_pandas(),
on=["label", "premise", "hypothesis"],
how="inner",
).shape[0],
)
#> train - test: 10
#> train - validation: 0
#> test - validation: 0
``` | [
-0.29186609387397766,
-0.6627394556999207,
0.1899091601371765,
0.4920392334461212,
-0.1177658811211586,
-0.04570484533905983,
-0.2249978482723236,
-0.1545741856098175,
0.6898257732391357,
0.5024040937423706,
-0.5152216553688049,
-0.5629522204399109,
-0.5860103964805603,
0.3886512517929077,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AmirulOm/lottie-urls | AmirulOm | 2022-10-25T10:12:14Z | 14 | 1 | null | [
"task_categories:image-segmentation",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:n<1K",
"source_datasets:original",
"license:unknown",
"region:us"
] | 2022-10-25T10:12:14Z | 2022-04-25T22:45:19.000Z | 2022-04-25T22:45:19 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language: []
license:
- unknown
multilinguality: []
pretty_name: lottie-uri
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
List of lottiefiles uri for research purposes
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
-0.47101855278015137,
-0.523065447807312,
0.07408524304628372,
0.348478764295578,
-0.21345970034599304,
0.14320869743824005,
-0.3059256374835968,
-0.3776730000972748,
0.6238911151885986,
0.7174004912376404,
-0.8416358232498169,
-1.2013750076293945,
-0.759159505367279,
0.06990587711334229,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Davincilee/closure_system_door_inner | Davincilee | 2022-04-29T20:59:50Z | 14 | 0 | null | [
"license:lgpl-3.0",
"region:us"
] | 2022-04-29T20:59:50Z | 2022-04-26T01:48:09.000Z | 2022-04-26T01:48:09 | ---
license: lgpl-3.0
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Ericblancosf/lenssubtechnique | Ericblancosf | 2022-04-26T02:10:25Z | 14 | 0 | null | [
"region:us"
] | 2022-04-26T02:10:25Z | 2022-04-26T02:09:44.000Z | 2022-04-26T02:09:44 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
eleldar/different_sub_normal_datasets | eleldar | 2022-06-16T11:19:15Z | 14 | 0 | null | [
"region:us"
] | 2022-06-16T11:19:15Z | 2022-04-26T06:32:15.000Z | 2022-04-26T06:32:15 | Dataset for API: https://github.com/eleldar/Translation | [
-0.14138083159923553,
-0.2420276701450348,
0.2107500582933426,
0.07818207889795303,
-0.24100111424922943,
0.1935921609401703,
-0.33351781964302063,
-0.34046581387519836,
0.5140613913536072,
0.8969407677650452,
-0.779017984867096,
-1.0235731601715088,
-0.46363726258277893,
0.206655248999595... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mrm8488/ImageNet1K-train | mrm8488 | 2022-04-28T11:06:11Z | 14 | 0 | null | [
"region:us"
] | 2022-04-28T11:06:11Z | 2022-04-27T20:03:48.000Z | 2022-04-27T20:03:48 | 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
``` | [
-1.0844253301620483,
-0.2456229031085968,
0.3232777416706085,
0.41045206785202026,
-0.14507359266281128,
0.3055281341075897,
0.16771180927753448,
-0.4816921055316925,
0.8262014985084534,
-0.2975088357925415,
-0.24258370697498322,
-0.4879564940929413,
-0.952782154083252,
0.585330605506897,
... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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