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f0f37162e31f17be4a703fc555be1a965b77adf5 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-66b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456336 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T14:31:27+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_test"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-66b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_test", "dataset_config": "mathemakitten--winobias_antistereotype_test", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-29T17:00:45+00:00 |
4ab783c3e7e2cc5ca9ea75ab922b856f096e6b9e | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-6.7b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456333 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T14:31:27+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_test"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-6.7b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_test", "dataset_config": "mathemakitten--winobias_antistereotype_test", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-29T14:47:19+00:00 |
8881f6b4ef7d33351a0e5b73d482b280bf35992e | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-2.7b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456332 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T14:31:27+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_test"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-2.7b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_test", "dataset_config": "mathemakitten--winobias_antistereotype_test", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-29T14:36:34+00:00 |
4f0ea713c9fbb0e90fb46605a9d6fa40045c0cb7 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-125m
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456329 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T14:31:27+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_test"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-125m", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_test", "dataset_config": "mathemakitten--winobias_antistereotype_test", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-29T14:32:08+00:00 |
26eec3ffb27c97bfd5b123dae4f046a6c6cb2676 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-1.3b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456331 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T14:31:27+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_test"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-1.3b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_test", "dataset_config": "mathemakitten--winobias_antistereotype_test", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-29T14:34:41+00:00 |
4cf687b19fb10893ab4f13a9e2bec3323150897b | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-350m
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456330 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T14:31:29+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_test"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-350m", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_test", "dataset_config": "mathemakitten--winobias_antistereotype_test", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-29T14:32:36+00:00 |
40811b7d45e7be647accbaad064231273e3d5ff0 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-30b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456335 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T14:31:29+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_test"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-30b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_test", "dataset_config": "mathemakitten--winobias_antistereotype_test", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-29T15:38:50+00:00 |
1201e7301176c674f1f05bd1d01787c919b1ea76 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-13b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456334 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T14:31:36+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_test"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-13b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_test", "dataset_config": "mathemakitten--winobias_antistereotype_test", "dataset_split": "test", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2022-09-29T14:59:04+00:00 |
9704434c1038783fb4eb69ffc76b029e2ea43643 | annotations_creators:
- no-annotation
language:
- en
language_creators:
- other
license:
- artistic-2.0
multilinguality:
- monolingual
pretty_name: m3 dataset (a dataset with my face in it)
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
task_ids: [] | Gr3en/m3 | [
"region:us"
] | 2022-09-29T15:07:05+00:00 | {} | 2022-09-29T16:13:42+00:00 |
35b7e3c042a42e312b44b2f327a889939436ed62 | Dopamina/dopamina | [
"license:artistic-2.0",
"region:us"
] | 2022-09-29T15:57:03+00:00 | {"license": "artistic-2.0"} | 2022-09-29T16:03:03+00:00 |
|
6e20e114326dd6e209339bc47f392d5906aeb931 | yes | Ivanrex/images | [
"region:us"
] | 2022-09-29T16:08:56+00:00 | {} | 2022-09-29T16:12:35+00:00 |
60fb7ce6cb24b741122dd9e40a5e59a0659181ab | Ivanrex/fotos | [
"region:us"
] | 2022-09-29T16:14:58+00:00 | {} | 2022-09-29T16:16:51+00:00 |
|
e83d8655bfe879dc84a5cc298550f0d4dfdf4d40 | linarez83/fotos_mias | [
"license:afl-3.0",
"region:us"
] | 2022-09-29T16:20:12+00:00 | {"license": "afl-3.0"} | 2022-09-29T16:20:12+00:00 |
|
d81cbab7d0392708d5371d3a4960e69261824db4 |
# Dataset Card for New Yorker Caption Contest Benchmarks
## 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:** [capcon.dev](https://www.capcon.dev)
- **Repository:** [https://github.com/jmhessel/caption_contest_corpus](https://github.com/jmhessel/caption_contest_corpus)
- **Paper:** [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293)
- **Leaderboard:** https://leaderboard.allenai.org/nycc-matching/
- **Point of Contact:** [email protected]
### Dataset Summary
See [capcon.dev](https://www.capcon.dev) for more!
Data from:
[Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293)
```
@inproceedings{hessel2023androids,
title={Do Androids Laugh at Electric Sheep? {Humor} ``Understanding''
Benchmarks from {The New Yorker Caption Contest}},
author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D. and Lee, Lillian
and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin},
booktitle={Proceedings of the ACL},
year={2023}
}
```
If you use this dataset, we would appreciate you citing our work, but also -- several other papers that we build this corpus upon. See [Citation Information](#citation-information).
We challenge AI models to "demonstrate understanding" of the
sophisticated multimodal humor of The New Yorker Caption Contest.
Concretely, we develop three carefully circumscribed tasks for which
it suffices (but is not necessary) to grasp potentially complex and
unexpected relationships between image and caption, and similarly
complex and unexpected allusions to the wide varieties of human
experience.
### Supported Tasks and Leaderboards
Three tasks are supported:
- "Matching:" a model must recognize a caption written about a cartoon (vs. options that were not);
- "Quality ranking:" a model must evaluate the quality of a caption by scoring it more highly than a lower quality option from the same contest;
- "Explanation:" a model must explain why a given joke is funny.
There are no official leaderboards (yet).
### Languages
English
## Dataset Structure
Here's an example instance from Matching:
```
{'caption_choices': ['Tell me about your childhood very quickly.',
"Believe me . . . it's what's UNDER the ground that's "
'most interesting.',
"Stop me if you've heard this one.",
'I have trouble saying no.',
'Yes, I see the train but I think we can beat it.'],
'contest_number': 49,
'entities': ['https://en.wikipedia.org/wiki/Rule_of_three_(writing)',
'https://en.wikipedia.org/wiki/Bar_joke',
'https://en.wikipedia.org/wiki/Religious_institute'],
'from_description': 'scene: a bar description: Two priests and a rabbi are '
'walking into a bar, as the bartender and another patron '
'look on. The bartender talks on the phone while looking '
'skeptically at the incoming crew. uncanny: The scene '
'depicts a very stereotypical "bar joke" that would be '
'unlikely to be encountered in real life; the skepticism '
'of the bartender suggests that he is aware he is seeing '
'this trope, and is explaining it to someone on the '
'phone. entities: Rule_of_three_(writing), Bar_joke, '
'Religious_institute. choices A: Tell me about your '
"childhood very quickly. B: Believe me . . . it's what's "
"UNDER the ground that's most interesting. C: Stop me if "
"you've heard this one. D: I have trouble saying no. E: "
'Yes, I see the train but I think we can beat it.',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=323x231 at 0x7F34F283E9D0>,
'image_description': 'Two priests and a rabbi are walking into a bar, as the '
'bartender and another patron look on. The bartender '
'talks on the phone while looking skeptically at the '
'incoming crew.',
'image_location': 'a bar',
'image_uncanny_description': 'The scene depicts a very stereotypical "bar '
'joke" that would be unlikely to be encountered '
'in real life; the skepticism of the bartender '
'suggests that he is aware he is seeing this '
'trope, and is explaining it to someone on the '
'phone.',
'instance_id': '21125bb8787b4e7e82aa3b0a1cba1571',
'label': 'C',
'n_tokens_label': 1,
'questions': ['What is the bartender saying on the phone in response to the '
'living, breathing, stereotypical bar joke that is unfolding?']}
```
The label "C" indicates that the 3rd choice in the `caption_choices` is correct.
Here's an example instance from Ranking (in the from pixels setting --- though, this is also available in the from description setting)
```
{'caption_choices': ['I guess I misunderstood when you said long bike ride.',
'Does your divorce lawyer have any other cool ideas?'],
'contest_number': 582,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=600x414 at 0x7F8FF9F96610>,
'instance_id': 'dd1c214a1ca3404aa4e582c9ce50795a',
'label': 'A',
'n_tokens_label': 1,
'winner_source': 'official_winner'}
```
the label indicates that the first caption choice ("A", here) in the `caption_choices` list was more highly rated.
Here's an example instance from Explanation:
```
{'caption_choices': 'The classics can be so intimidating.',
'contest_number': 752,
'entities': ['https://en.wikipedia.org/wiki/Literature',
'https://en.wikipedia.org/wiki/Solicitor'],
'from_description': 'scene: a road description: Two people are walking down a '
'path. A number of giant books have surrounded them. '
'uncanny: There are book people in this world. entities: '
'Literature, Solicitor. caption: The classics can be so '
'intimidating.',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=800x706 at 0x7F90003D0BB0>,
'image_description': 'Two people are walking down a path. A number of giant '
'books have surrounded them.',
'image_location': 'a road',
'image_uncanny_description': 'There are book people in this world.',
'instance_id': 'eef9baf450e2fab19b96facc128adf80',
'label': 'A play on the word intimidating --- usually if the classics (i.e., '
'classic novels) were to be intimidating, this would mean that they '
'are intimidating to read due to their length, complexity, etc. But '
'here, they are surrounded by anthropomorphic books which look '
'physically intimidating, i.e., they are intimidating because they '
'may try to beat up these people.',
'n_tokens_label': 59,
'questions': ['What do the books want?']}
```
The label is an explanation of the joke, which serves as the autoregressive target.
### Data Instances
See above
### Data Fields
See above
### Data Splits
Data splits can be accessed as:
```
from datasets import load_dataset
dset = load_dataset("jmhessel/newyorker_caption_contest", "matching")
dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking")
dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation")
```
Or, in the from pixels setting, e.g.,
```
from datasets import load_dataset
dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking_from_pixels")
```
Because the dataset is small, we reported in 5-fold cross-validation setting initially. The default splits are split 0. You can access the other splits, e.g.:
```
from datasets import load_dataset
# the 4th data split
dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation_4")
```
## Dataset Creation
Full details are in the paper.
### Curation Rationale
See the paper for rationale/motivation.
### Source Data
See citation below. We combined 3 sources of data, and added significant annotations of our own.
#### Initial Data Collection and Normalization
Full details are in the paper.
#### Who are the source language producers?
We paid crowdworkers $15/hr to annotate the corpus.
In addition, significant annotation efforts were conducted by the authors of this work.
### Annotations
Full details are in the paper.
#### Annotation process
Full details are in the paper.
#### Who are the annotators?
A mix of crowdworks and authors of this paper.
### Personal and Sensitive Information
Has been redacted from the dataset. Images are published in the New Yorker already.
## Considerations for Using the Data
### Social Impact of Dataset
It's plausible that humor could perpetuate negative stereotypes. The jokes in this corpus are a mix of crowdsourced entries that are highly rated, and ones published in the new yorker.
### Discussion of Biases
Humor is subjective, and some of the jokes may be considered offensive. The images may contain adult themes and minor cartoon nudity.
### Other Known Limitations
More details are in the paper
## Additional Information
### Dataset Curators
The dataset was curated by researchers at AI2
### Licensing Information
The annotations we provide are CC-BY-4.0. See www.capcon.dev for more info.
### Citation Information
```
@article{hessel2022androids,
title={Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest},
author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin},
journal={arXiv preprint arXiv:2209.06293},
year={2022}
}
```
Our data contributions are:
- The cartoon-level annotations;
- The joke explanations;
- and the framing of the tasks
We release these data we contribute under CC-BY (see DATASET_LICENSE). If you find this data useful in your work, in addition to citing our contributions, please also cite the following, from which the cartoons/captions in our corpus are derived:
```
@misc{newyorkernextmldataset,
author={Jain, Lalit and Jamieson, Kevin and Mankoff, Robert and Nowak, Robert and Sievert, Scott},
title={The {N}ew {Y}orker Cartoon Caption Contest Dataset},
year={2020},
url={https://nextml.github.io/caption-contest-data/}
}
@inproceedings{radev-etal-2016-humor,
title = "Humor in Collective Discourse: Unsupervised Funniness Detection in The {New Yorker} Cartoon Caption Contest",
author = "Radev, Dragomir and
Stent, Amanda and
Tetreault, Joel and
Pappu, Aasish and
Iliakopoulou, Aikaterini and
Chanfreau, Agustin and
de Juan, Paloma and
Vallmitjana, Jordi and
Jaimes, Alejandro and
Jha, Rahul and
Mankoff, Robert",
booktitle = "LREC",
year = "2016",
}
@inproceedings{shahaf2015inside,
title={Inside jokes: Identifying humorous cartoon captions},
author={Shahaf, Dafna and Horvitz, Eric and Mankoff, Robert},
booktitle={KDD},
year={2015},
}
``` | jmhessel/newyorker_caption_contest | [
"task_categories:image-to-text",
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:visual-question-answering",
"task_categories:other",
"task_categories:text2text-generation",
"task_ids:multi-class-classification",
"task_ids:language-modeling",
"task_ids:visual-question-answering",
"task_ids:explanation-generation",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
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"humor",
"caption contest",
"new yorker",
"arxiv:2209.06293",
"region:us"
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1571}, {"name": "validation", "num_bytes": 28550057.0, "num_examples": 513}, {"name": "test", "num_bytes": 33718821.0, "num_examples": 531}], "download_size": 136230399, "dataset_size": 161277009.43}], "configs": [{"config_name": "explanation", "data_files": [{"split": "train", "path": "explanation/train-*"}, {"split": "validation", "path": "explanation/validation-*"}, {"split": "test", "path": "explanation/test-*"}]}, {"config_name": "explanation_1", "data_files": [{"split": "train", "path": "explanation_1/train-*"}, {"split": "validation", "path": "explanation_1/validation-*"}, {"split": "test", "path": "explanation_1/test-*"}]}, {"config_name": "explanation_2", "data_files": [{"split": "train", "path": "explanation_2/train-*"}, {"split": "validation", "path": "explanation_2/validation-*"}, {"split": "test", "path": "explanation_2/test-*"}]}, {"config_name": "explanation_3", "data_files": [{"split": "train", "path": "explanation_3/train-*"}, {"split": "validation", "path": "explanation_3/validation-*"}, {"split": "test", "path": "explanation_3/test-*"}]}, {"config_name": "explanation_4", "data_files": [{"split": "train", "path": "explanation_4/train-*"}, {"split": "validation", "path": "explanation_4/validation-*"}, {"split": "test", "path": "explanation_4/test-*"}]}, {"config_name": "explanation_from_pixels", "data_files": [{"split": "train", "path": "explanation_from_pixels/train-*"}, {"split": "validation", "path": "explanation_from_pixels/validation-*"}, {"split": "test", "path": "explanation_from_pixels/test-*"}]}, {"config_name": "explanation_from_pixels_1", "data_files": [{"split": "train", "path": "explanation_from_pixels_1/train-*"}, {"split": "validation", "path": "explanation_from_pixels_1/validation-*"}, {"split": "test", "path": "explanation_from_pixels_1/test-*"}]}, {"config_name": "explanation_from_pixels_2", "data_files": [{"split": "train", "path": "explanation_from_pixels_2/train-*"}, {"split": "validation", "path": "explanation_from_pixels_2/validation-*"}, {"split": "test", "path": "explanation_from_pixels_2/test-*"}]}, {"config_name": "explanation_from_pixels_3", "data_files": [{"split": "train", "path": "explanation_from_pixels_3/train-*"}, {"split": "validation", "path": "explanation_from_pixels_3/validation-*"}, {"split": "test", "path": "explanation_from_pixels_3/test-*"}]}, {"config_name": "explanation_from_pixels_4", "data_files": [{"split": "train", "path": "explanation_from_pixels_4/train-*"}, {"split": "validation", "path": "explanation_from_pixels_4/validation-*"}, {"split": "test", "path": "explanation_from_pixels_4/test-*"}]}, {"config_name": "matching", "data_files": [{"split": "train", "path": "matching/train-*"}, {"split": "validation", "path": "matching/validation-*"}, {"split": "test", "path": "matching/test-*"}]}, {"config_name": "matching_1", "data_files": [{"split": "train", "path": "matching_1/train-*"}, {"split": "validation", "path": "matching_1/validation-*"}, {"split": "test", "path": "matching_1/test-*"}]}, {"config_name": "matching_2", "data_files": [{"split": "train", "path": "matching_2/train-*"}, {"split": "validation", "path": "matching_2/validation-*"}, {"split": "test", "path": "matching_2/test-*"}]}, {"config_name": "matching_3", "data_files": [{"split": "train", "path": "matching_3/train-*"}, {"split": "validation", "path": "matching_3/validation-*"}, {"split": "test", "path": "matching_3/test-*"}]}, {"config_name": "matching_4", "data_files": [{"split": "train", "path": "matching_4/train-*"}, {"split": "validation", "path": "matching_4/validation-*"}, {"split": "test", "path": "matching_4/test-*"}]}, {"config_name": "matching_from_pixels", "data_files": [{"split": "train", "path": "matching_from_pixels/train-*"}, {"split": "validation", "path": "matching_from_pixels/validation-*"}, {"split": "test", "path": "matching_from_pixels/test-*"}]}, {"config_name": "matching_from_pixels_1", "data_files": [{"split": "train", "path": "matching_from_pixels_1/train-*"}, {"split": "validation", "path": "matching_from_pixels_1/validation-*"}, {"split": "test", "path": "matching_from_pixels_1/test-*"}]}, {"config_name": "matching_from_pixels_2", "data_files": [{"split": "train", "path": "matching_from_pixels_2/train-*"}, {"split": "validation", "path": "matching_from_pixels_2/validation-*"}, {"split": "test", "path": "matching_from_pixels_2/test-*"}]}, {"config_name": "matching_from_pixels_3", "data_files": [{"split": "train", "path": "matching_from_pixels_3/train-*"}, {"split": "validation", "path": "matching_from_pixels_3/validation-*"}, {"split": "test", "path": "matching_from_pixels_3/test-*"}]}, {"config_name": "matching_from_pixels_4", "data_files": [{"split": "train", "path": "matching_from_pixels_4/train-*"}, {"split": "validation", "path": "matching_from_pixels_4/validation-*"}, {"split": "test", "path": "matching_from_pixels_4/test-*"}]}, {"config_name": "ranking", "data_files": [{"split": "train", "path": "ranking/train-*"}, {"split": "validation", "path": "ranking/validation-*"}, {"split": "test", "path": "ranking/test-*"}]}, {"config_name": "ranking_1", "data_files": [{"split": "train", "path": "ranking_1/train-*"}, {"split": "validation", "path": "ranking_1/validation-*"}, {"split": "test", "path": "ranking_1/test-*"}]}, {"config_name": "ranking_2", "data_files": [{"split": "train", "path": "ranking_2/train-*"}, {"split": "validation", "path": "ranking_2/validation-*"}, {"split": "test", "path": "ranking_2/test-*"}]}, {"config_name": "ranking_3", "data_files": [{"split": "train", "path": "ranking_3/train-*"}, {"split": "validation", "path": "ranking_3/validation-*"}, {"split": "test", "path": "ranking_3/test-*"}]}, {"config_name": "ranking_4", "data_files": [{"split": "train", "path": "ranking_4/train-*"}, {"split": "validation", "path": "ranking_4/validation-*"}, {"split": "test", "path": "ranking_4/test-*"}]}, {"config_name": "ranking_from_pixels", "data_files": [{"split": "train", "path": "ranking_from_pixels/train-*"}, {"split": "validation", "path": "ranking_from_pixels/validation-*"}, {"split": "test", "path": "ranking_from_pixels/test-*"}]}, {"config_name": "ranking_from_pixels_1", "data_files": [{"split": "train", "path": "ranking_from_pixels_1/train-*"}, {"split": "validation", "path": "ranking_from_pixels_1/validation-*"}, {"split": "test", "path": "ranking_from_pixels_1/test-*"}]}, {"config_name": "ranking_from_pixels_2", "data_files": [{"split": "train", "path": "ranking_from_pixels_2/train-*"}, {"split": "validation", "path": "ranking_from_pixels_2/validation-*"}, {"split": "test", "path": "ranking_from_pixels_2/test-*"}]}, {"config_name": "ranking_from_pixels_3", "data_files": [{"split": "train", "path": "ranking_from_pixels_3/train-*"}, {"split": "validation", "path": "ranking_from_pixels_3/validation-*"}, {"split": "test", "path": "ranking_from_pixels_3/test-*"}]}, {"config_name": "ranking_from_pixels_4", "data_files": [{"split": "train", "path": "ranking_from_pixels_4/train-*"}, {"split": "validation", "path": "ranking_from_pixels_4/validation-*"}, {"split": "test", "path": "ranking_from_pixels_4/test-*"}]}]} | 2023-12-22T19:13:58+00:00 |
b72dd5646b9a7d3b3eb60ab0f73479d1869c67ef | Grizz/gothic | [
"license:afl-3.0",
"region:us"
] | 2022-09-29T16:56:32+00:00 | {"license": "afl-3.0"} | 2022-09-29T16:57:56+00:00 |
|
baf5387cd27f305a07ce560081cac4b525526355 | marcosfevre/stromberg | [
"license:cc-by-4.0",
"region:us"
] | 2022-09-29T17:04:35+00:00 | {"license": "cc-by-4.0"} | 2022-09-30T18:02:56+00:00 |
|
427887a50d4bb85b86723440d15fe4889bd5f020 | Gianpaolo/ORGANIC_TYPOGRAPHY | [
"region:us"
] | 2022-09-29T18:58:51+00:00 | {} | 2022-09-29T19:31:32+00:00 |
|
4b887241ee3f0c2efa31a7f04596bd1042a0ef05 | badmaiky/images | [
"license:openrail",
"region:us"
] | 2022-09-29T19:20:42+00:00 | {"license": "openrail"} | 2022-09-29T19:22:21+00:00 |
|
d2e593d645e8b7d71ab76738be13269f96b0139b | # AutoTrain Dataset for project: github-emotion-surprise
## Dataset Description
Dataset used in the paper: Imran et al., ["Data Augmentation for Improving Emotion Recognition in Software Engineering Communication"](https://arxiv.org/abs/2208.05573), ASE-2022.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_id": 704844644,
"text": "This change doesn't affect anything but makes the code more clear. If you look at the line about, `currentUrlTree` is set to `urlAfterRedirects`.",
"feat_Anger": 0,
"feat_Love": 0,
"feat_Fear": 0,
"feat_Joy": 1,
"feat_Sadness": 0,
"target": 0
},
{
"feat_id": 886568180,
"text": "Thanks very much for your feedback [USER] Your point is totally fair. My intention was to highlight that camelCase or dash-case class names are perfectly fine to use in Angular templates. Most people, especially beginners, do not know that and end up using the `ngClass` directive. Do you think that rewording the alert towards that direction would make sense?",
"feat_Anger": 0,
"feat_Love": 1,
"feat_Fear": 0,
"feat_Joy": 0,
"feat_Sadness": 0,
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_id": "Value(dtype='int64', id=None)",
"text": "Value(dtype='string', id=None)",
"feat_Anger": "Value(dtype='int64', id=None)",
"feat_Love": "Value(dtype='int64', id=None)",
"feat_Fear": "Value(dtype='int64', id=None)",
"feat_Joy": "Value(dtype='int64', id=None)",
"feat_Sadness": "Value(dtype='int64', id=None)",
"target": "ClassLabel(num_classes=2, names=['0', '1'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1600 |
| valid | 400 |
| imranraad/github-emotion-surprise | [
"task_categories:text-classification",
"arxiv:2208.05573",
"doi:10.57967/hf/0050",
"region:us"
] | 2022-09-29T20:03:25+00:00 | {"task_categories": ["text-classification"]} | 2022-10-20T09:18:22+00:00 |
8d818753c4d4b3541433a20d2a7008e4e3cfa427 | pictures | wallyg/Pictures | [
"region:us"
] | 2022-09-29T20:04:21+00:00 | {} | 2022-09-29T20:20:59+00:00 |
3d4df382ad4507ff652d99e244bb3e3c1532d0a0 | # AutoTrain Dataset for project: github-emotion-love
## Dataset Description
Dataset used in the paper: Imran et al., ["Data Augmentation for Improving Emotion Recognition in Software Engineering Communication"](https://arxiv.org/abs/2208.05573), ASE-2022.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_id": 704844644,
"text": "This change doesn't affect anything but makes the code more clear. If you look at the line about, `currentUrlTree` is set to `urlAfterRedirects`.",
"feat_Anger": 0,
"target": 0,
"feat_Fear": 0,
"feat_Joy": 1,
"feat_Sadness": 0,
"feat_Surprise": 0
},
{
"feat_id": 886568180,
"text": "Thanks very much for your feedback [USER] Your point is totally fair. My intention was to highlight that camelCase or dash-case class names are perfectly fine to use in Angular templates. Most people, especially beginners, do not know that and end up using the `ngClass` directive. Do you think that rewording the alert towards that direction would make sense?",
"feat_Anger": 0,
"target": 1,
"feat_Fear": 0,
"feat_Joy": 0,
"feat_Sadness": 0,
"feat_Surprise": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_id": "Value(dtype='int64', id=None)",
"text": "Value(dtype='string', id=None)",
"feat_Anger": "Value(dtype='int64', id=None)",
"target": "ClassLabel(num_classes=2, names=['0', '1'], id=None)",
"feat_Fear": "Value(dtype='int64', id=None)",
"feat_Joy": "Value(dtype='int64', id=None)",
"feat_Sadness": "Value(dtype='int64', id=None)",
"feat_Surprise": "Value(dtype='int64', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1600 |
| valid | 400 |
| imranraad/github-emotion-love | [
"task_categories:text-classification",
"arxiv:2208.05573",
"doi:10.57967/hf/0049",
"region:us"
] | 2022-09-29T20:10:30+00:00 | {"task_categories": ["text-classification"]} | 2022-10-20T09:18:07+00:00 |
ea8c479a02e299af56a9ac2a5371cdea39d24e5d | Enoch2090/github_semantic_search | [
"license:gpl-3.0",
"region:us"
] | 2022-09-29T20:15:05+00:00 | {"license": "gpl-3.0"} | 2023-02-19T20:32:27+00:00 |
|
cd2e95ae08dc82f53588e91d63a8817dd4f5b553 | Jose888888/helloeee | [
"license:openrail",
"region:us"
] | 2022-09-29T21:22:00+00:00 | {"license": "openrail"} | 2022-11-07T19:15:12+00:00 |
|
4ef74c321f3c474a1549b42545cda4e74b3870ae | mvb6969/Fotos_mvb6969 | [
"license:openrail",
"region:us"
] | 2022-09-29T21:42:46+00:00 | {"license": "openrail"} | 2022-09-29T23:00:41+00:00 |
|
6a151c5d80f3c0d00af267e030daca4f42df9012 | Imagenes:
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me1.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me2.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me3.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me4.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me5.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me6.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me7.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me8.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me9.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me10.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me11.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me12.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me13.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me14.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me15.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me16.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me17.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me18.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me19.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me20.jpg",
"https://huggingface.co/datasets/sati93/fotos/resolve/main/me21.jpg",
Configuracion:
instance_prompt: sati
prior_preservation_class_prompt: person
| sati93/fotos | [
"region:us"
] | 2022-09-29T23:28:59+00:00 | {} | 2022-10-02T19:19:26+00:00 |
2b8181cec3b249fea71bc0f09abef2861b020417 | jamesluc007/test | [
"region:us"
] | 2022-09-29T23:45:39+00:00 | {} | 2022-09-30T00:04:38+00:00 |
|
627e5cc137bcd577a9769bbb108ff97c65cd8aac | scratch directory for storing image datasets which are processed through a clip embedding model!
---
license: mit
---
| murphyk/dogs-cats-small-clip-embedding | [
"region:us"
] | 2022-09-30T00:38:19+00:00 | {} | 2022-09-30T02:46:33+00:00 |
0cdde8c9b4daeb3b70b9269cbe1fbbf613b927a6 | trevfran/perfil | [
"license:other",
"region:us"
] | 2022-09-30T00:50:24+00:00 | {"license": "other"} | 2022-09-30T01:07:57+00:00 |
|
1fdb3a86e97900ed57af51b62880ba504e6d91a8 | Nian/DreamBooth_Test | [
"license:mit",
"region:us"
] | 2022-09-30T01:12:05+00:00 | {"license": "mit"} | 2022-09-30T01:19:29+00:00 |
|
61a5f43d7178da5e7a43a372acbeab6212db5e96 | virfuji/connor | [
"license:afl-3.0",
"region:us"
] | 2022-09-30T01:21:48+00:00 | {"license": "afl-3.0"} | 2022-09-30T01:25:04+00:00 |
|
5088e9b8a20afb6797b4ddff4d7b014b573818bb | joemmile/Lia | [
"license:cc",
"region:us"
] | 2022-09-30T03:06:24+00:00 | {"license": "cc"} | 2022-09-30T16:01:58+00:00 |
|
e99e27c90f20307ebbefd7e79e35255a62de3118 |
# Dataset Card for Reflections in Peer Counseling
## 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: Automatic Reflection Generation for Peer-to-Peer Counseling**
- **Point of Contact: [email protected]**
### Dataset Summary
The dataset derives from conversations between clients and counselors on a large peer-to-peer online counseling service. There are a total of 1061 observations across training and testing datasets, with 50 additional randomly sampled examples used in defining the few-shot learning prompt or for validation purposes in tuning hyperparameters, thus totaling 1111 observations across these sets. These observations were sourced from a larger dataset consisting of annotations of several different clinical counseling skills. We thus focus on the annotations of counselor reflections. The counselor reflections were annotated at utterance level with counselor verbal behaviors using the Motivational Interviewing Treatment Integrity 4.2 (MITI) and the Motivational Interviewing Skill Code 2.5 (MISC) manuals. Thus, the entire dataset consists of conversational context-counselor reflection pairs.
### Supported Tasks and Leaderboards
The dataset was used for conditioning and tuning generative models for generating reflection statements in the domain of peer-to-peer counseling.
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
Each instance consists of the chat room id of the conversation in which the dialogue occurred, the prompt which is the conversational context that immediately precedes the counselor reflection (including previous utterances from either the client or counselor up until and including the most recent prior client message that immediately followed a counselorโs message), and the completion which is the counselor reflection.
```
{
'chat_id': "1234567",
'prompt': "Client: I'm 19, he's 25. He's not very considerate of how I feel but says he cares about me and loves me.\nCounselor:",
'completion': " The words are easy, actions are needed. Guys who are 25 just desire to have different experiences.\n\n",
}
```
### Data Fields
* `chat_id`: an integer defining the chat id of the conversation
* `prompt`: a string corresponding to the conversational context preceding the counselor reflection with the messages separated by new line characters and each utterance prepended by 'Client:' or 'Counselor:'. The string ends with 'Counselor:' to indicate that it is followed by the counselor completion described below.
* `completion`: a string corresponding to the counselor reflection
### Data Splits
The dataset is split into training, testing, and a small set of 50 examples used either for designing the few-shot learning prompt or tuning hyperparameters. 911 examples were used for training. 350 of these examples also constitute a reduced training set used in comparative experiments. 150 examples were used for testing. 50 of these testing examples (randomly selected) were used in the human evaluation. We ensured that the chat identifiers for messages in the test set uniquely differed from those included in the training set.
## Dataset Creation
### Curation Rationale
Reflective listening is a critical skill in peer-to-peer counseling that is only effective when tailored to the context. Thus, we wanted to home in on this particular skill and explore the potential of state-of-the-art language models for text generation in this domain.
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by filtering the larger dataset of utterances annotated for many different counseling skills to only those counselor messages annotated as reflections. Then, the prompt instances were created by identifying the preceding messages for each of these counselor reflection instances. After the prompts were initially created, prompts with less than or equal to five words were removed.
The author created reference reflections for each of the 350 training example prompts in the reduced training set and each of the 150 testing example prompts. In creating a reference reflection given each conversational context, the author intended to simulate responding to the client in roughly the same time a counselor would as if this turn was embedded in a conversation the client was having with the author. This gauging of time is based on the authorโs experience in volunteering as a counselor at crisis hotlines. It is possible that the reference reflections were created in roughly even less time than an average counselor response given that there were hundreds of conversational contexts for which reflections needed to be created.
#### Who are the source language producers?
The 'client' messages are utterances of those seeking mental health support on a large online counseling service platform. The 'counselor' messages are utterances of minimally-trained peer counselors of this large online counseling service.
For each of the 350 training example prompts in the reduced training set and each of the 150 testing example prompts, a reference reflection was also created by the author.
### Annotations
#### Annotation process
The human evaluation examined text of generative models fine-tuned on the full training set, a reduced training set, and reference reflections; a few-shot learning model; the actual counselor; and the reference reflection.
We administered a survey through Amazon Mechanical Turk Developer Sandbox. 50 of the testing prompts were provided along with the corresponding six response sources. Provided with the conversational context, the annotators evaluated responses based on three criteria: fluency, resemblance of reflection, and overall preference. Thus, for each context, evaluators measured the fluency, reflection resemblance, and overall preference for all six candidate responses.
We used a variation of Efficient Annotation of Scalar Labels (EASL), a hybrid approach between direct assessment and online pairwise ranking aggregation and rank-based magnitude estimation. Evaluators saw all six responses at once (without knowledge of each responseโs origin) and used a sliding scale from 1 to 5 to rate the responses based on each of the three dimensions. The order of the model responses for each conversational context was randomized. We provided examples of response ratings for ratings of 1 and 5 on the overall fluency and reflection resemblance dimensions. However, we did not include an example for overall preference, noting its subjectivity. The order of the model responses for each conversational context was randomized. We provided examples of response ratings for ratings of 1 and 5 on the overall fluency and reflection resemblance dimensions. However, we did not include an example for overall preference, noting its subjectivity.
Fluency refers to the response's overall fluency and human-likeness. In the instructions, we noted non-capitalized words and colloquial language are acceptable and not to be considered fluency errors. Reflection resemblance refers to whether the response captures and returns to the client something the client has said. Overall preference refers to the extent to which the evaluator likes the response.
Using Krippendorffโs alpha, we measured inter-annotator agreement, obtaining alpha values of -0.0369, 0.557, and 0.358 for overall fluency, reflection resemblance, and overall preference, respectively. Although these agreement values are low, the 0.557 inter-annotator agreement we obtained for reflection resemblance is notably higher than the inter-annotator agreement obtained for reflection likeness in the most relevant prior work.
#### Who are the annotators?
The three annotators recruited for the human evaluation were familiar with counseling reflections. All three annotators have worked with this large online counseling service dataset with IRB approval. They are quite familiar with motivational interviewing codes, annotating messages and using large language models for mass labeling.
### Personal and Sensitive Information
Due to the sensitive nature of this dataset and privacy concerns, we are unable to publicly share the data.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset of reflections in peer-to-peer counseling can be used as a reference point in understanding and evaluating counselor clinical skills and furthering the potential of language technology to be applied in this space. Given the sensitive nature of the mental health care context and the minimal training of these counselors, the use of such data requires care in understanding the limitations of technology defined based on this language.
### Discussion of Biases
Much of the language of conversations on this online counseling service platform is very informal and some client and counselor utterances may also contain pejorative language.
As for the generated text assessed in the human evaluation of this work, it is important to note that GPT-3 was trained on over 45 terabytes of data from the internet and books, and large volumes of data collected from online sources will inevitably contain biases that may be captured. There may thus be inadvertent discrimination against subclasses of particular protected groups. Using generated responses as a source of guidance rather than using generative systems as the counselors themselves may be able to balance the benefits and risks of using artificial intelligence in delicate mental health settings. It is imperative that such systems are not misused by companies seeking to maximize efficiency and minimize cost.
The reference reflections in this work were created by the author, whose experience with counseling and motivational interviewing derives from over one hundred hours of training at a teen-to-teen crisis hotline and textline service and experience through a research fellowship developing and user testing a platform for nurses to practice and grow their motivational interviewing skills. Therefore, the reference reflections may not be as clinically precise as are possible from a medical professional, and the diversity of reflections is inherently limited.
### Other Known Limitations
## Additional Information
### Dataset Curators
Developed by Emma O'Neil, Joรฃo Sedoc, Diyi Yang, Haiyi Zhu, Lyle Ungar.
### Licensing Information
### Citation Information
### Contributions
Thanks to [@emoneil](https://github.com/emoneil) for adding this dataset. | emoneil/reflections-in-peer-counseling | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:expert-generated",
"size_categories:1K<n<10K",
"gpt3",
"natural language processing",
"natural language generation",
"peer counseling",
"region:us"
] | 2022-09-30T03:21:28+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": [], "language": [], "license": [], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["summarization", "text-generation", "conversational"], "task_ids": ["dialogue-generation"], "pretty_name": "Reflections in Peer Counseling", "tags": ["gpt3", "natural language processing", "natural language generation", "peer counseling"]} | 2022-10-14T02:59:04+00:00 |
6685505e1e3c02ac0483398e633922b31de89fb0 |
## Dataset Description
A segmentation dataset for anime character
My project: [anime-segmentation](https://github.com/SkyTNT/anime-segmentation)
### Dataset Summary
| Dir | Description | Format | Images |
| ---- | ---- | ---- | ---- |
| bg | background images | jpg | 8057 |
| fg | foreground images, transparent background | png | 11802 |
| imgs | real images with background and foreground| jpg | 1111 |
| masks| labels for imgs | jpg | 1111 |
Total size: 18GB
### Collection Method
Collect background from [character_bg_seg_data](https://github.com/ShuhongChen/bizarre-pose-estimator#download)
Collect foreground from danbooru website.
Collect imgs and masks from [AniSeg](https://github.com/jerryli27/AniSeg#about-the-models) and danbooru website.
I use [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to restore the background images.
I clean the dataset using [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) first then manually, to make sue all foreground is anime character.
### Contributions
Thanks to [@SkyTNT](https://github.com/SkyTNT) for adding this dataset.
Thanks to [@ShuhongChen](https://github.com/ShuhongChen) for [character_bg_seg_data](https://github.com/ShuhongChen/bizarre-pose-estimator#download)
Thanks to [@jerryli27](https://github.com/jerryli27) for [AniSeg](https://github.com/jerryli27/AniSeg#about-the-models)
| skytnt/anime-segmentation | [
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"size_categories:10K<n<100K",
"source_datasets:original",
"license:cc0-1.0",
"region:us"
] | 2022-09-30T04:27:06+00:00 | {"annotations_creators": [], "language_creators": [], "language": [], "license": ["cc0-1.0"], "multilinguality": [], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["image-segmentation"], "task_ids": ["semantic-segmentation"], "pretty_name": "Anime Segmentation", "tags": []} | 2022-10-03T00:35:40+00:00 |
2fc722a09b37bee7ea8bbf850f59f004c7bb5c15 |
All eight of datasets in ESC can be downloaded and prepared in just a single line of code through the Hugging Face Datasets library:
```python
from datasets import load_dataset
librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", split="train")
```
- `"esc-benchmark"`: the repository namespace. This is fixed for all ESC datasets.
- `"librispeech"`: the dataset name. This can be changed to any of any one of the eight datasets in ESC to download that dataset.
- `split="train"`: the split. Set this to one of train/validation/test to generate a specific split. Omit the `split` argument to generate all splits for a dataset.
The datasets are full prepared, such that the audio and transcription files can be used directly in training/evaluation scripts.
## Dataset Information
A data point can be accessed by indexing the dataset object loaded through `load_dataset`:
```python
print(librispeech[0])
```
A typical data point comprises the path to the audio file and its transcription. Also included is information of the dataset from which the sample derives and a unique identifier name:
```python
{
'dataset': 'librispeech',
'audio': {'path': '/home/esc-bencher/.cache/huggingface/datasets/downloads/extracted/d2da1969fe9e7d06661b5dc370cf2e3c119a14c35950045bcb76243b264e4f01/374-180298-0000.flac',
'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ...,
-2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
'sampling_rate': 16000},
'text': 'chapter sixteen i might have told you of the beginning of this liaison in a few lines but i wanted you to see every step by which we came i to agree to whatever marguerite wished',
'id': '374-180298-0000'
}
```
### Data Fields
- `dataset`: name of the ESC dataset from which the sample is taken.
- `audio`: a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `text`: the transcription of the audio file.
- `id`: unique id of the data sample.
### Data Preparation
#### Audio
The audio for all ESC datasets is segmented into sample lengths suitable for training ASR systems. The Hugging Face datasets library decodes audio files on the fly, reading the segments and converting them to a Python arrays. Consequently, no further preparation of the audio is required to be used in training/evaluation scripts.
Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`.
#### Transcriptions
The transcriptions corresponding to each audio file are provided in their 'error corrected' format. No transcription pre-processing is applied to the text, only necessary 'error correction' steps such as removing junk tokens (_<unk>_) or converting symbolic punctuation to spelled out form (_<comma>_ to _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts.
Transcriptions are provided for training and validation splits. The transcriptions are **not** provided for the test splits. The ESC benchmark requires you to generate predictions for the test sets and upload them to https://huggingface.co/spaces/esc-benchmark/esc for scoring.
### Access
All eight of the datasets in ESC are accessible and licensing is freely available. Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages:
* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
* GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
* SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
### Diagnostic Dataset
ESC contains a small, 8h diagnostic dataset of in-domain validation data with newly annotated transcriptions. The audio data is sampled from each of the ESC validation sets, giving a range of different domains and speaking styles. The transcriptions are annotated according to a consistent style guide with two formats: normalised and un-normalised. The dataset is structured in the same way as the ESC dataset, by grouping audio-transcription samples according to the dataset from which they were taken. We encourage participants to use this dataset when evaluating their systems to quickly assess performance on a range of different speech recognition conditions. For more information, visit: [esc-bench/esc-diagnostic-dataset](https://huggingface.co/datasets/esc-bench/esc-diagnostic-datasets).
## LibriSpeech
The LibriSpeech corpus is a standard large-scale corpus for assessing ASR systems. It consists of approximately 1,000 hours of narrated audiobooks from the [LibriVox](https://librivox.org) project. It is licensed under CC-BY-4.0.
Example Usage:
```python
librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech")
```
Train/validation splits:
- `train` (combination of `train.clean.100`, `train.clean.360` and `train.other.500`)
- `validation.clean`
- `validation.other`
Test splits:
- `test.clean`
- `test.other`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", subconfig="clean.100")
```
- `clean.100`: 100 hours of training data from the 'clean' subset
- `clean.360`: 360 hours of training data from the 'clean' subset
- `other.500`: 500 hours of training data from the 'other' subset
## Common Voice
Common Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The English subset of contains approximately 1,400 hours of audio data from speakers of various nationalities, accents and different recording conditions. It is licensed under CC0-1.0.
Example usage:
```python
common_voice = load_dataset("esc-benchmark/esc-datasets", "common_voice", use_auth_token=True)
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## VoxPopuli
VoxPopuli s a large-scale multilingual speech corpus consisting of political data sourced from 2009-2020 European Parliament event recordings. The English subset contains approximately 550 hours of speech largely from non-native English speakers. It is licensed under CC0.
Example usage:
```python
voxpopuli = load_dataset("esc-benchmark/esc-datasets", "voxpopuli")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## TED-LIUM
TED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0.
Example usage:
```python
tedlium = load_dataset("esc-benchmark/esc-datasets", "tedlium")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## GigaSpeech
GigaSpeech is a multi-domain English speech recognition corpus created from audiobooks, podcasts and YouTube. We provide the large train set (2,500 hours) and the standard validation and test splits. It is licensed under apache-2.0.
Example usage:
```python
gigaspeech = load_dataset("esc-benchmark/esc-datasets", "gigaspeech", use_auth_token=True)
```
Training/validation splits:
- `train` (`l` subset of training data (2,500 h))
- `validation`
Test splits:
- `test`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
gigaspeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="xs", use_auth_token=True)
```
- `xs`: extra-small subset of training data (10 h)
- `s`: small subset of training data (250 h)
- `m`: medium subset of training data (1,000 h)
- `xl`: extra-large subset of training data (10,000 h)
## SPGISpeech
SPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement.
Loading the dataset requires authorization.
Example usage:
```python
spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", use_auth_token=True)
```
Training/validation splits:
- `train` (`l` subset of training data (~5,000 h))
- `validation`
Test splits:
- `test`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="s", use_auth_token=True)
```
- `s`: small subset of training data (~200 h)
- `m`: medium subset of training data (~1,000 h)
## Earnings-22
Earnings-22 is a 119-hour corpus of English-language earnings calls collected from global companies, with speakers of many different nationalities and accents. It is licensed under CC-BY-SA-4.0.
Example usage:
```python
earnings22 = load_dataset("esc-benchmark/esc-datasets", "earnings22")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## AMI
The AMI Meeting Corpus consists of 100 hours of meeting recordings from multiple recording devices synced to a common timeline. It is licensed under CC-BY-4.0.
Example usage:
```python
ami = load_dataset("esc-benchmark/esc-datasets", "ami")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test` | esc-bench/esc-datasets | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|librispeech_asr",
"source_datasets:extended|common_voice",
"language:en",
"license:cc-by-4.0",
"license:apache-2.0",
"license:cc0-1.0",
"license:cc-by-nc-3.0",
"license:other",
"asr",
"benchmark",
"speech",
"esc",
"region:us"
] | 2022-09-30T07:32:42+00:00 | {"annotations_creators": ["expert-generated", "crowdsourced", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["cc-by-4.0", "apache-2.0", "cc0-1.0", "cc-by-nc-3.0", "other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "1M<n<10M"], "source_datasets": ["original", "extended|librispeech_asr", "extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "esc-datasets", "tags": ["asr", "benchmark", "speech", "esc"], "extra_gated_prompt": "Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. \nTo do so, fill in the access forms on the specific datasets' pages:\n * Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0\n * GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech\n * SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech", "extra_gated_fields": {"I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset": "checkbox", "I hereby confirm that I have accepted the terms of usages on GigaSpeech page": "checkbox", "I hereby confirm that I have accepted the terms of usages on SPGISpeech page": "checkbox"}} | 2022-10-21T13:34:49+00:00 |
909545ffdb20e7d356b95c561f54afa9e12f7a3c | neibla/debates | [
"license:mit",
"region:us"
] | 2022-09-30T07:48:52+00:00 | {"license": "mit"} | 2022-09-30T07:51:33+00:00 |
|
56d4be6b894907642fa235e00540b257b303b2fc | skatemonke/bartek | [
"license:unknown",
"region:us"
] | 2022-09-30T08:05:48+00:00 | {"license": "unknown"} | 2022-09-30T09:54:05+00:00 |
|
73cb1384b467451a0c32c1851b712a7e90a9bc57 |
# Dataset Card for PP4AV
## 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)
- [Dataset Creation](#dataset-creation)
- [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/khaclinh/pp4av
- **Repository:**
- **Paper:** [PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving]
- **Point of Contact:** [email protected]
### Dataset Summary
PP4AV is the first public dataset with faces and license plates annotated with driving scenarios. P4AV provides 3,447 annotated driving images for both faces and license plates. For normal camera data, dataset sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. This dataset use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. PP4AV dataset can be used as a benchmark suite (evaluating dataset) for data anonymization models in autonomous driving.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its face and license plate annotations.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x19FA12186D8>, 'objects': {
'bbox': [
[0 0.230078 0.317081 0.239062 0.331367],
[1 0.5017185 0.0306425 0.5185935 0.0410975],
[1 0.695078 0.0710145 0.7109375 0.0863355],
[1 0.4089065 0.31646 0.414375 0.32764],
[0 0.1843745 0.403416 0.201093 0.414182],
[0 0.7132 0.3393474 0.717922 0.3514285]
]
}
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `objects`: a dictionary of face and license plate bounding boxes present on the image
- `bbox`: the bounding box of each face and license plate (in the [yolo](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#yolo) format). Basically, each row in annotation `.txt` file for each image `.png` file consists of data in format: `<object-class> <x_center> <y_center> <width> <height>`:
- `object-class`: integer number of object from 0 to 1, where 0 indicate face object, and 1 indicate licese plate object
- `x_center`: normalized x-axis coordinate of the center of the bounding box.
`x_center = <absolute_x_center> / <image_width>`
- `y_center`: normalized y-axis coordinate of the center of the bounding box.
`y_center = <absolute_y_center> / <image_height>`
- `width`: normalized width of the bounding box.
`width = <absolute_width> / <image_width>`
- `height`: normalized wheightdth of the bounding box.
`height = <absolute_height> / <image_height>`
- Example lines in YOLO v1.1 format `.txt' annotation file:
` 1 0.716797 0.395833 0.216406 0.147222
0 0.687109 0.379167 0.255469 0.158333
1 0.420312 0.395833 0.140625 0.166667
`
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The objective of PP4AV is to build a benchmark dataset that can be used to evaluate face and license plate detection models for autonomous driving. For normal camera data, we sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. We focus on sampling data in urban areas rather than highways in order to provide sufficient samples of license plates and pedestrians. The images in PP4AV were sampled from **6** European cities at various times of day, including nighttime. The source data from 6 cities in European was described as follow:
- `Paris`: This subset contains **1450** images of the car driving down a Parisian street during the day. The video frame rate is 30 frames per second. The video is longer than one hour. We cut a shorter video for sampling and annotation. The original video can be found at the following URL:
URL: [paris_youtube_video](https://www.youtube.com/watch?v=nqWtGWymV6c)
- `Netherland day time`: This subset consists of **388** images of Hague, Amsterdam city in day time. The image of this subset are sampled from the bellow original video:
URL: [netherland_youtube_video](https://www.youtube.com/watch?v=Xuo4uCZxNrE)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour.
- `Netherland night time`: This subset consists of **824** images of Hague, Amsterdam city in night time sampled by the following original video:
URL: [netherland_youtube_video](https://www.youtube.com/watch?v=eAy9eHsynhM)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour.
- `Switzerland`: This subset consists of **372** images of Switzerland sampled by the following video:
URL: [switzerland_youtube_video](https://www.youtube.com/watch?v=0iw5IP94m0Q)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than one hour.
- `Zurich`: This subset consists of **50** images of Zurich city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
- `Stuttgart`: This subset consists of **69** images of Stuttgart city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
- `Strasbourg`: This subset consists of **50** images of Strasbourg city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
We use the fisheye images from the WoodScape dataset to select **244** images from the front, rear, left, and right cameras for fisheye camera data.
The source of fisheye data for sampling is located at WoodScape's [Fisheye images](https://woodscape.valeo.com/download).
In total, **3,447** images were selected and annotated in PP4AV.
### Annotations
#### Annotation process
Annotators annotate facial and license plate objects in images. For facial objects, bounding boxes are defined by all detectable human faces from the forehead to the chin to the ears. Faces were labelled with diverse sizes, skin tones, and faces partially obscured by a transparent material, such as a car windshield. For license plate objects, bounding boxes consists of all recognizable license plates with high variability, such as different sizes, countries, vehicle types (motorcycle, automobile, bus, truck), and occlusions by other vehicles. License plates were annotated for vehicles involved in moving traffic. To ensure the quality of annotation, there are two-step process for annotation. In the first phase, two teams of annotators will independently annotate identical image sets. After their annotation output is complete, a merging method based on the IoU scores between the two bounding boxes of the two annotations will be applied. Pairs of annotations with IoU scores above a threshold will be merged and saved as a single annotation. Annotated pairs with IoU scores below a threshold will be considered conflicting. In the second phase, two teams of reviewers will inspect the conflicting pairs of annotations for revision before a second merging method similar to the first is applied. The results of these two phases will be combined to form the final annotation. All work is conducted on the CVAT tool https://github.com/openvinotoolkit/cvat.
#### Who are the annotators?
Vantix Data Science team
### 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
Linh Trinh
### Licensing Information
[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
### Citation Information
```
@article{PP4AV2022,
title = {PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving},
author = {Linh Trinh, Phuong Pham, Hoang Trinh, Nguyen Bach, Dung Nguyen, Giang Nguyen, Huy Nguyen},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year = {2023}
}
```
### Contributions
Thanks to [@khaclinh](https://github.com/khaclinh) for adding this dataset.
| khaclinh/testdata | [
"task_categories:object-detection",
"task_ids:face-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:en",
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2022-09-30T08:12:25+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended"], "task_categories": ["object-detection"], "task_ids": ["face-detection", "license-plate-detection"], "pretty_name": "PP4AV"} | 2023-11-10T23:16:51+00:00 |
ec76e2bfdd7bfbd9d04b24b5d0cbefb424e0b5c9 | Eric pics | Speedy02/eric | [
"region:us"
] | 2022-09-30T08:20:51+00:00 | {} | 2022-09-30T08:55:02+00:00 |
56584831fefeb7d6cef37df192c05f4ad8b8fc00 | This dataset contains images for the classification of bees and ants | delima87/beesvsants | [
"region:us"
] | 2022-09-30T08:26:04+00:00 | {} | 2022-09-30T08:34:41+00:00 |
3191d1da8882b20445722fe811b70533412e2173 | # Dataset Card for "MultiTACRED"
## Dataset Description
- **Homepage:** [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED)
- **Paper:** [MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset](https://arxiv.org/abs/2305.04582)
- **Point of Contact:** See [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED)
- **Size of downloaded dataset files:** 15.4KB (TACRED-Revisited), 3.7 MB (Re-TACRED)
- **Size of the generated dataset:** 1.7 GB (all languages, all versions)
- **Total amount of disk used:** 1.7 GB (all languages, all versions)
### Dataset Summary
MultiTACRED is a multilingual version of the large-scale [TAC Relation Extraction Dataset](https://nlp.stanford.edu/projects/tacred).
It covers 12 typologically diverse languages from 9 language families, and was created by the
[Speech & Language Technology group of DFKI](https://www.dfki.de/slt) by machine-translating the instances of the
original TACRED dataset and automatically projecting their entity annotations. For details of the original TACRED's
data collection and annotation process, see the [Stanford paper](https://aclanthology.org/D17-1004/). Translations are
syntactically validated by checking the correctness of the XML tag markup. Any translations with an invalid tag
structure, e.g. missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances).
Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish,
Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers.
Please see [our ACL paper](https://aclanthology.org/2023.acl-long.210/) for full details.
NOTE: This Datasetreader supports a reduced version of the original TACRED JSON format with the following changes:
- Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
The motivation for this is that we want to support additional languages, for which these fields were not required
or available. The reader expects the specification of a language-specific configuration specifying the variant
(original, revisited or retacred) and the language (as a two-letter iso code).
The DatasetReader changes the offsets of the following fields, to conform with standard Python usage (see
_generate_examples()):
- subj_end to subj_end + 1 (make end offset exclusive)
- obj_end to obj_end + 1 (make end offset exclusive)
NOTE 2: The MultiTACRED dataset offers an additional 'split', namely the backtranslated test data (translated to a
target language and then back to English). To access this split, use dataset['backtranslated_test'].
You can find the TACRED dataset reader for the English version of the dataset at
[https://huggingface.co/datasets/DFKI-SLT/tacred](https://huggingface.co/datasets/DFKI-SLT/tacred).
### Supported Tasks and Leaderboards
- **Tasks:** Relation Classification
- **Leaderboards:** [https://paperswithcode.com/sota/relation-extraction-on-multitacred](https://paperswithcode.com/sota/relation-extraction-on-multitacred)
### Languages
The languages in the dataset are Arabic, German, English, Spanish, Finnish, French, Hindi, Hungarian, Japanese, Polish, Russian, Turkish, and Chinese.
All languages except English are machine-translated using either Deepl's or Google's translation APIs.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 15.4KB (TACRED-Revisited), 3.7 MB (Re-TACRED)
- **Size of the generated dataset:** 1.7 GB (all languages, all versions)
- **Total amount of disk used:** 1.7 GB (all languages, all versions)
An example of 'train' looks as follows:
```json
{
"id": "61b3a5c8c9a882dcfcd2",
"token": ["Tom", "Thabane", "trat", "im", "Oktober", "letzten", "Jahres", "zurรผck", ",", "um", "die", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", "zu", "grรผnden", ",", "die", "mit", "17", "Abgeordneten", "das", "Wort", "ergriff", ",", "woraufhin", "der", "konstitutionelle", "Monarch", "Kรถnig", "Letsie", "III.", "das", "Parlament", "auflรถste", "und", "Neuwahlen", "ansetzte", "."],
"relation": "org:founded_by",
"subj_start": 11,
"subj_end": 13,
"obj_start": 0,
"obj_end": 1,
"subj_type": "ORGANIZATION",
"obj_type": "PERSON"
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: the instance id of this sentence, a `string` feature.
- `token`: the list of tokens of this sentence, a `list` of `string` features.
- `relation`: the relation label of this instance, a `string` classification label.
- `subj_start`: the 0-based index of the start token of the relation subject mention, an `รฌnt` feature.
- `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive, an `รฌnt` feature.
- `subj_type`: the NER type of the subject mention, among the types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
- `obj_start`: the 0-based index of the start token of the relation object mention, an `รฌnt` feature.
- `obj_end`: the 0-based index of the end token of the relation object mention, exclusive, an `รฌnt` feature.
- `obj_type`: the NER type of the object mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
### Data Splits
To miminize dataset bias, TACRED is stratified across years in which the TAC KBP challenge was run.
Languages statistics for the splits differ because not all instances could be translated with the
subject and object entity markup still intact, these were discarded.
| Language | Train | Dev | Test | Backtranslated Test | Translation Engine |
| ----- | ------ | ----- | ---- | ---- | ---- |
| en | 68,124 | 22,631 | 15,509 | - | - |
| ar | 67,736 | 22,502 | 15,425 | 15,425 | Google |
| de | 67,253 | 22,343 | 15,282 | 15,079 | DeepL |
| es | 65,247 | 21,697 | 14,908 | 14,688 | DeepL |
| fi | 66,751 | 22,268 | 15,083 | 14,462 | DeepL |
| fr | 66,856 | 22,298 | 15,237 | 15,088 | DeepL |
| hi | 67,751 | 22,511 | 15,440 | 15,440 | Google |
| hu | 67,766 | 22,519 | 15,436 | 15,436 | Google |
| ja | 61,571 | 20,290 | 13,701 | 12,913 | DeepL |
| pl | 68,124 | 22,631 | 15,509 | 15,509 | Google |
| ru | 66,413 | 21,998 | 14,995 | 14,703 | DeepL |
| tr | 67,749 | 22,510 | 15,429 | 15,429 | Google |
| zh | 65,260 | 21,538 | 14,694 | 14,021 | DeepL |
## Dataset Creation
### Curation Rationale
To enable more research on multilingual Relation Extraction, we generate translations of the TAC relation extraction
dataset using DeepL and Google Translate.
### Source Data
#### Initial Data Collection and Normalization
The instances of this dataset are sentences from the
[original TACRED dataset](https://nlp.stanford.edu/projects/tacred/), which in turn
are sampled from the [corpus](https://catalog.ldc.upenn.edu/LDC2018T03) used in the yearly
[TAC Knowledge Base Population (TAC KBP) challenges](https://tac.nist.gov/2017/KBP/index.html).
#### Who are the source language producers?
Newswire and web texts collected for the [TAC Knowledge Base Population (TAC KBP) challenges](https://tac.nist.gov/2017/KBP/index.html).
### Annotations
#### Annotation process
See the Stanford paper, the TACRED Revisited paper, and the Re-TACRED paper, plus their appendices, for
details on the original annotation process. The translated versions do not change the original labels.
Translations were tokenized with language-specific Spacy models (Spacy 3.1, 'core_news/web_sm' models)
or Trankit (Trankit 1.1.0) when there was no Spacy model for a given language (Hungarian, Turkish, Arabic, Hindi).
#### Who are the annotators?
The original TACRED dataset was annotated by crowd workers, see the [TACRED paper](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf).
### Personal and Sensitive Information
The [authors](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf) of the original TACRED dataset
have not stated measures that prevent collecting sensitive or offensive text. Therefore, we do
not rule out the possible risk of sensitive/offensive content in the translated data.
## Considerations for Using the Data
### Social Impact of Dataset
not applicable
### Discussion of Biases
The dataset is drawn from web and newswire text, and thus reflects any biases of these original
texts, as well as biases introduced by the MT models.
### Other Known Limitations
not applicable
## Additional Information
### Dataset Curators
The dataset was created by members of the
[DFKI SLT team: Leonhard Hennig, Philippe Thomas, Sebastian Mรถller, Gabriel Kressin](https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology/speech-and-language-technology-staff-members)
### Licensing Information
To respect the copyright of the underlying TACRED dataset, MultiTACRED is released via the
Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)).
You can download MultiTACRED from the [LDC MultiTACRED webpage](https://catalog.ldc.upenn.edu/TODO).
If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.
### Citation Information
The original dataset:
```
@inproceedings{zhang2017tacred,
author = {Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D.},
booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)},
title = {Position-aware Attention and Supervised Data Improve Slot Filling},
url = {https://nlp.stanford.edu/pubs/zhang2017tacred.pdf},
pages = {35--45},
year = {2017}
}
```
For the revised version, please also cite:
```
@inproceedings{alt-etal-2020-tacred,
title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task",
author = "Alt, Christoph and
Gabryszak, Aleksandra and
Hennig, Leonhard",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.142",
doi = "10.18653/v1/2020.acl-main.142",
pages = "1558--1569",
}
```
For the Re-TACRED version, please also cite:
```
@inproceedings{DBLP:conf/aaai/StoicaPP21,
author = {George Stoica and
Emmanouil Antonios Platanios and
Barnab{\'{a}}s P{\'{o}}czos},
title = {Re-TACRED: Addressing Shortcomings of the {TACRED} Dataset},
booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9,
2021},
pages = {13843--13850},
publisher = {{AAAI} Press},
year = {2021},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17631},
}
```
### Contributions
Thanks to [@leonhardhennig](https://github.com/leonhardhennig) for adding this dataset. | DFKI-SLT/multitacred | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"size_categories:100K<n<1M",
"source_datasets:DFKI-NLP/tacred",
"language:ar",
"language:de",
"language:es",
"language:fi",
"language:fr",
"language:hi",
"language:hu",
"language:ja",
"language:pl",
"language:ru",
"language:tr",
"language:zh",
"license:other",
"relation extraction",
"arxiv:2305.04582",
"region:us"
] | 2022-09-30T10:31:31+00:00 | {"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["found"], "language": ["ar", "de", "es", "fi", "fr", "hi", "hu", "ja", "pl", "ru", "tr", "zh"], "license": "other", "size_categories": ["100K<n<1M"], "source_datasets": ["DFKI-NLP/tacred"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "paperswithcode_id": "multitacred", "pretty_name": "MultiTACRED - Multilingual TAC Relation Extraction Dataset", "license_details": "https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf", "tags": ["relation extraction"], "dataset_info": [{"config_name": "original-ar", "features": [{"name": "id", "dtype": "string"}, {"name": "token", "sequence": "string"}, {"name": "subj_start", "dtype": "int32"}, {"name": "subj_end", "dtype": "int32"}, {"name": "subj_type", "dtype": {"class_label": {"names": {"0": "LOCATION", "1": "ORGANIZATION", "2": "PERSON", "3": "DATE", "4": "MONEY", "5": "PERCENT", "6": "TIME", "7": "CAUSE_OF_DEATH", "8": "CITY", "9": "COUNTRY", "10": "CRIMINAL_CHARGE", "11": "EMAIL", "12": "HANDLE", "13": "IDEOLOGY", "14": "NATIONALITY", "15": "RELIGION", "16": "STATE_OR_PROVINCE", "17": "TITLE", "18": "URL", "19": "NUMBER", "20": "ORDINAL", "21": "MISC", "22": "DURATION", "23": "O"}}}}, {"name": "obj_start", "dtype": "int32"}, {"name": "obj_end", "dtype": "int32"}, {"name": "obj_type", "dtype": {"class_label": {"names": {"0": "LOCATION", "1": "ORGANIZATION", "2": "PERSON", "3": "DATE", "4": "MONEY", "5": "PERCENT", "6": "TIME", "7": "CAUSE_OF_DEATH", "8": "CITY", "9": "COUNTRY", "10": "CRIMINAL_CHARGE", "11": "EMAIL", "12": "HANDLE", "13": "IDEOLOGY", "14": "NATIONALITY", "15": "RELIGION", "16": "STATE_OR_PROVINCE", "17": "TITLE", "18": "URL", "19": "NUMBER", "20": "ORDINAL", "21": "MISC", "22": "DURATION", "23": "O"}}}}, {"name": "relation", "dtype": {"class_label": {"names": {"0": "no_relation", "1": "org:alternate_names", "2": "org:city_of_headquarters", "3": "org:country_of_headquarters", "4": "org:dissolved", "5": "org:founded", "6": "org:founded_by", "7": "org:member_of", "8": "org:members", "9": "org:number_of_employees/members", "10": "org:parents", "11": "org:political/religious_affiliation", "12": "org:shareholders", "13": "org:stateorprovince_of_headquarters", "14": "org:subsidiaries", "15": "org:top_members/employees", "16": "org:website", "17": "per:age", "18": "per:alternate_names", "19": "per:cause_of_death", "20": "per:charges", "21": "per:children", "22": "per:cities_of_residence", "23": "per:city_of_birth", "24": "per:city_of_death", "25": "per:countries_of_residence", "26": "per:country_of_birth", "27": "per:country_of_death", "28": "per:date_of_birth", "29": "per:date_of_death", "30": "per:employee_of", "31": "per:origin", "32": "per:other_family", "33": "per:parents", "34": "per:religion", "35": "per:schools_attended", "36": "per:siblings", "37": "per:spouse", "38": "per:stateorprovince_of_birth", "39": "per:stateorprovince_of_death", "40": "per:stateorprovinces_of_residence", "41": "per:title"}}}}], "splits": [{"name": "train", "num_bytes": 32371641, "num_examples": 67736}, {"name": "test", "num_bytes": 6895001, "num_examples": 15425}, {"name": "validation", "num_bytes": 10353930, "num_examples": 22502}, {"name": "backtranslated_test", "num_bytes": 5687302, "num_examples": 15425}], "download_size": 0, "dataset_size": 55307874}, {"config_name": "revisited-ar", "features": [{"name": "id", "dtype": "string"}, {"name": "token", "sequence": "string"}, {"name": "subj_start", "dtype": "int32"}, {"name": "subj_end", "dtype": "int32"}, {"name": "subj_type", "dtype": {"class_label": {"names": {"0": "LOCATION", "1": "ORGANIZATION", "2": "PERSON", "3": "DATE", "4": "MONEY", "5": "PERCENT", "6": "TIME", "7": "CAUSE_OF_DEATH", "8": "CITY", "9": "COUNTRY", "10": "CRIMINAL_CHARGE", "11": "EMAIL", "12": "HANDLE", "13": "IDEOLOGY", "14": "NATIONALITY", "15": "RELIGION", "16": "STATE_OR_PROVINCE", "17": "TITLE", "18": "URL", "19": "NUMBER", "20": "ORDINAL", "21": "MISC", "22": "DURATION", "23": "O"}}}}, {"name": "obj_start", "dtype": "int32"}, {"name": "obj_end", "dtype": "int32"}, {"name": "obj_type", "dtype": {"class_label": {"names": {"0": "LOCATION", "1": "ORGANIZATION", "2": "PERSON", "3": "DATE", "4": "MONEY", "5": "PERCENT", "6": "TIME", "7": "CAUSE_OF_DEATH", "8": "CITY", "9": "COUNTRY", "10": "CRIMINAL_CHARGE", "11": "EMAIL", "12": "HANDLE", "13": "IDEOLOGY", "14": "NATIONALITY", "15": "RELIGION", "16": "STATE_OR_PROVINCE", "17": "TITLE", "18": "URL", "19": "NUMBER", "20": "ORDINAL", "21": "MISC", "22": "DURATION", "23": "O"}}}}, {"name": "relation", "dtype": {"class_label": {"names": {"0": "no_relation", "1": "org:alternate_names", "2": "org:city_of_headquarters", "3": "org:country_of_headquarters", "4": "org:dissolved", "5": "org:founded", "6": "org:founded_by", "7": "org:member_of", "8": "org:members", "9": "org:number_of_employees/members", "10": "org:parents", "11": "org:political/religious_affiliation", "12": "org:shareholders", "13": "org:stateorprovince_of_headquarters", "14": "org:subsidiaries", "15": "org:top_members/employees", "16": "org:website", "17": "per:age", "18": "per:alternate_names", "19": "per:cause_of_death", "20": "per:charges", "21": "per:children", "22": "per:cities_of_residence", "23": "per:city_of_birth", "24": "per:city_of_death", "25": "per:countries_of_residence", "26": "per:country_of_birth", "27": "per:country_of_death", "28": "per:date_of_birth", "29": "per:date_of_death", "30": "per:employee_of", "31": "per:origin", "32": "per:other_family", "33": "per:parents", "34": "per:religion", "35": "per:schools_attended", "36": "per:siblings", "37": "per:spouse", "38": "per:stateorprovince_of_birth", "39": "per:stateorprovince_of_death", "40": "per:stateorprovinces_of_residence", "41": "per:title"}}}}], "splits": [{"name": "train", "num_bytes": 32371641, 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"CRIMINAL_CHARGE", "11": "EMAIL", "12": "HANDLE", "13": "IDEOLOGY", "14": "NATIONALITY", "15": "RELIGION", "16": "STATE_OR_PROVINCE", "17": "TITLE", "18": "URL", "19": "NUMBER", "20": "ORDINAL", "21": "MISC", "22": "DURATION", "23": "O"}}}}, {"name": "obj_start", "dtype": "int32"}, {"name": "obj_end", "dtype": "int32"}, {"name": "obj_type", "dtype": {"class_label": {"names": {"0": "LOCATION", "1": "ORGANIZATION", "2": "PERSON", "3": "DATE", "4": "MONEY", "5": "PERCENT", "6": "TIME", "7": "CAUSE_OF_DEATH", "8": "CITY", "9": "COUNTRY", "10": "CRIMINAL_CHARGE", "11": "EMAIL", "12": "HANDLE", "13": "IDEOLOGY", "14": "NATIONALITY", "15": "RELIGION", "16": "STATE_OR_PROVINCE", "17": "TITLE", "18": "URL", "19": "NUMBER", "20": "ORDINAL", "21": "MISC", "22": "DURATION", "23": "O"}}}}, {"name": "relation", "dtype": {"class_label": {"names": {"0": "no_relation", "1": "org:alternate_names", "2": "org:city_of_branch", "3": "org:country_of_branch", "4": "org:dissolved", "5": "org:founded", "6": "org:founded_by", "7": "org:member_of", "8": "org:members", "9": "org:number_of_employees/members", "10": "org:political/religious_affiliation", "11": "org:shareholders", "12": "org:stateorprovince_of_branch", "13": "org:top_members/employees", "14": "org:website", "15": "per:age", "16": "per:cause_of_death", "17": "per:charges", "18": "per:children", "19": "per:cities_of_residence", "20": "per:city_of_birth", "21": "per:city_of_death", "22": "per:countries_of_residence", "23": "per:country_of_birth", "24": "per:country_of_death", "25": "per:date_of_birth", "26": "per:date_of_death", "27": "per:employee_of", "28": "per:identity", "29": "per:origin", "30": "per:other_family", "31": "per:parents", "32": "per:religion", "33": "per:schools_attended", "34": "per:siblings", "35": "per:spouse", "36": "per:stateorprovince_of_birth", "37": "per:stateorprovince_of_death", "38": "per:stateorprovinces_of_residence", "39": "per:title"}}}}], "splits": [{"name": "train", "num_bytes": 22440419, "num_examples": 56049}, {"name": "test", "num_bytes": 4717593, "num_examples": 12718}, {"name": "validation", "num_bytes": 7200681, "num_examples": 18642}, {"name": "backtranslated_test", "num_bytes": 4441386, "num_examples": 12127}], "download_size": 3702157, "dataset_size": 38800079}]} | 2024-01-17T09:16:51+00:00 |
ca9324836eefa4c1d7bc835afcaace6759dc3202 | The data contains three different vehicles from CCSA (https://www.ccsa.gmu.edu/models/):
A Toyota Yaris
A Chevy Silverado
And an ADS vehicle
These vehicles were tested at different speeds, and the binout files were stored.
The car models were used to develop an AI that could estimate a full frontal impact for different cars at different speeds.
This can then be used to predict the force of an impact for an Autonomous car simulator. | holen/Finite_element_crash_data | [
"license:apache-2.0",
"region:us"
] | 2022-09-30T10:43:24+00:00 | {"license": "apache-2.0"} | 2022-09-30T15:35:49+00:00 |
3b04f22b6b00133646c814aac26785a428acdaad |
This is the Faroese Common Crawl corpus. The largest dataset of mono-lingual Faroese text, it was extracted from the Common Crawl.
If you find this dataset useful, please cite
```
@inproceedings{snaebjarnarson-etal-2023-transfer,
title = "{T}ransfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese",
author = "Snรฆbjarnarson, Vรฉsteinn and
Simonsen, Annika and
Glavaลก, Goran and
Vuliฤ, Ivan",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = "may 22--24",
year = "2023",
address = "Tรณrshavn, Faroe Islands",
publisher = {Link{\"o}ping University Electronic Press, Sweden},
}
``` | vesteinn/FC3 | [
"language:fo",
"license:cc",
"region:us"
] | 2022-09-30T11:09:39+00:00 | {"language": ["fo"], "license": "cc", "pretty_name": "FC3"} | 2023-03-23T15:51:34+00:00 |
22ed42ff72e12eac2938306f120987e9b3e4c711 |
# Dataset Card for SMG-NFT
## Examples
## Citation
| pking/SMG-NFT | [
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-09-30T11:20:49+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["other"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": [], "task_categories": ["text-to-image"], "task_ids": [], "pretty_name": "SMG-NFT", "tags": []} | 2022-10-04T18:31:50+00:00 |
ef1661775d746e0844b299164773db733bdc0bf6 | # 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:** [The official homepage of Sprรฅkbanken](https://spraakbanken.gu.se/resurser/superlim/)
- **Repository:**
- **Paper:**[SwedishGLUE โ Towards a Swedish Test Set for Evaluating Natural Language Understanding Models](https://gup.ub.gu.se/publication/299130?lang=sv)
- **Leaderboard:** [To be implemented]
- **Point of Contact:**[[email protected]]([email protected])
### Dataset Summary
SuperLim 2.0 is a continuation of SuperLim 1.0, which aims for a standardized suite for evaluation and analysis of Swedish natural language understanding systems. The projects is inspired by the GLUE/SuperGLUE projects from which the name is derived: "lim" is the Swedish translation of "glue".
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Swedish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
Most datasets have a train, dev and test split. However, there are a few (`supersim`, `sweanalogy` and `swesat-synonyms`) who only have a train and test split. The diagnostic tasks `swediagnostics` and `swewinogender` only have a test split, but they could be evaluated on models trained on `swenli` since they are also NLI-based.
## 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
To cite as a whole, use the standard reference. If you use or reference individual resources, cite the references specific for these resources:
Standard reference:
To appear in EMNLP 2023, citation will come soon.
Dataset references:
[More information needed]
Thanks to [Felix Morger](https://github.com/felixhultin) for adding this dataset. | sbx/superlim-2 | [
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:token-classification",
"task_ids:sentiment-analysis",
"task_ids:acceptability-classification",
"task_ids:closed-domain-qa",
"task_ids:word-sense-disambiguation",
"task_ids:coreference-resolution",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"language:sv",
"region:us"
] | 2022-09-30T11:21:49+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["sv"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["multiple-choice", "text-classification", "question-answering", "sentence-similarity", "token-classification"], "task_ids": ["sentiment-analysis", "acceptability-classification", "closed-domain-qa", "word-sense-disambiguation", "coreference-resolution"], "pretty_name": "A standardized suite for evaluation and analysis of Swedish natural language understanding systems.", "tags": []} | 2023-10-12T07:10:39+00:00 |
3655d3cbaad4028f787282b2ada55967aabac9c1 |
# Dataset Card for NIH Chest X-ray dataset
## 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:** [NIH Chest X-ray Dataset of 10 Common Thorax Disease Categories](https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345)
- **Repository:**
- **Paper:** [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://arxiv.org/abs/1705.02315)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
_ChestX-ray dataset comprises 112,120 frontal-view X-ray images of 30,805 unique patients with the text-mined fourteen disease image labels (where each image can have multi-labels), mined from the associated radiological reports using natural language processing. Fourteen common thoracic pathologies include Atelectasis, Consolidation, Infiltration, Pneumothorax, Edema, Emphysema, Fibrosis, Effusion, Pneumonia, Pleural_thickening, Cardiomegaly, Nodule, Mass and Hernia, which is an extension of the 8 common disease patterns listed in our CVPR2017 paper. Note that original radiology reports (associated with these chest x-ray studies) are not meant to be publicly shared for many reasons. The text-mined disease labels are expected to have accuracy >90%.Please find more details and benchmark performance of trained models based on 14 disease labels in our arxiv paper: [1705.02315](https://arxiv.org/abs/1705.02315)_
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/95db46f21d556880cf0ecb11d45d5ba0b58fcb113c9a0fff2234eba8f74fe22a/images/00000798_022.png',
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=1024x1024 at 0x7F2151B144D0>,
'labels': [9, 3]}
```
### Data Fields
The data instances have the following fields:
- `image_file_path` a `str` with the image path
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label.
<details>
<summary>Class Label Mappings</summary>
```json
{
"No Finding": 0,
"Atelectasis": 1,
"Cardiomegaly": 2,
"Effusion": 3,
"Infiltration": 4,
"Mass": 5,
"Nodule": 6,
"Pneumonia": 7,
"Pneumothorax": 8,
"Consolidation": 9,
"Edema": 10,
"Emphysema": 11,
"Fibrosis": 12,
"Pleural_Thickening": 13,
"Hernia": 14
}
```
</details>
**Label distribution on the dataset:**
| labels | obs | freq |
|:-------------------|------:|-----------:|
| No Finding | 60361 | 0.426468 |
| Infiltration | 19894 | 0.140557 |
| Effusion | 13317 | 0.0940885 |
| Atelectasis | 11559 | 0.0816677 |
| Nodule | 6331 | 0.0447304 |
| Mass | 5782 | 0.0408515 |
| Pneumothorax | 5302 | 0.0374602 |
| Consolidation | 4667 | 0.0329737 |
| Pleural_Thickening | 3385 | 0.023916 |
| Cardiomegaly | 2776 | 0.0196132 |
| Emphysema | 2516 | 0.0177763 |
| Edema | 2303 | 0.0162714 |
| Fibrosis | 1686 | 0.0119121 |
| Pneumonia | 1431 | 0.0101104 |
| Hernia | 227 | 0.00160382 |
### Data Splits
| |train| test|
|-------------|----:|----:|
|# of examples|86524|25596|
**Label distribution by dataset split:**
| labels | ('Train', 'obs') | ('Train', 'freq') | ('Test', 'obs') | ('Test', 'freq') |
|:-------------------|-------------------:|--------------------:|------------------:|-------------------:|
| No Finding | 50500 | 0.483392 | 9861 | 0.266032 |
| Infiltration | 13782 | 0.131923 | 6112 | 0.164891 |
| Effusion | 8659 | 0.082885 | 4658 | 0.125664 |
| Atelectasis | 8280 | 0.0792572 | 3279 | 0.0884614 |
| Nodule | 4708 | 0.0450656 | 1623 | 0.0437856 |
| Mass | 4034 | 0.038614 | 1748 | 0.0471578 |
| Consolidation | 2852 | 0.0272997 | 1815 | 0.0489654 |
| Pneumothorax | 2637 | 0.0252417 | 2665 | 0.0718968 |
| Pleural_Thickening | 2242 | 0.0214607 | 1143 | 0.0308361 |
| Cardiomegaly | 1707 | 0.0163396 | 1069 | 0.0288397 |
| Emphysema | 1423 | 0.0136211 | 1093 | 0.0294871 |
| Edema | 1378 | 0.0131904 | 925 | 0.0249548 |
| Fibrosis | 1251 | 0.0119747 | 435 | 0.0117355 |
| Pneumonia | 876 | 0.00838518 | 555 | 0.0149729 |
| Hernia | 141 | 0.00134967 | 86 | 0.00232012 |
## 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]
### License and attribution
There are no restrictions on the use of the NIH chest x-ray images. However, the dataset has the following attribution requirements:
- Provide a link to the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC
- Include a citation to the CVPR 2017 paper (see Citation information section)
- Acknowledge that the NIH Clinical Center is the data provider
### Citation Information
```
@inproceedings{Wang_2017,
doi = {10.1109/cvpr.2017.369},
url = {https://doi.org/10.1109%2Fcvpr.2017.369},
year = 2017,
month = {jul},
publisher = {{IEEE}
},
author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}
}
```
### Contributions
Thanks to [@alcazar90](https://github.com/alcazar90) for adding this dataset.
| alkzar90/NIH-Chest-X-ray-dataset | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:unknown",
"arxiv:1705.02315",
"region:us"
] | 2022-09-30T11:45:52+00:00 | {"annotations_creators": ["machine-generated", "expert-generated"], "language_creators": ["machine-generated", "expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "chestx-ray14", "pretty_name": "NIH-CXR14"} | 2022-11-22T20:10:52+00:00 |
aa33b87297442d3bf9aa64ac8db2f1f14bd76b4f |
# Dataset Card for "XL-Sum-FI"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/TurkuNLP/xlsum-fi
- **Point of Contact:** [Filip Ginter](mailto:[email protected])
### Dataset Summary
This dataset is a DeepL -based machine translation of a part of the English section of the XLSum dataset:[https://github.com/csebuetnlp/xl-sum](https://github.com/csebuetnlp/xl-sum) In the present version, only examples where the full version is at most 10x the summary in length are included. We might translate more later.
### Supported Tasks and Leaderboards
### Languages
- `finnish`
## Dataset Structure
### Data Instances
One example from the `Finnish` dataset is given below in JSON format.
```
{
"id": "technology-17657859",
"url": "https://www.bbc.com/news/technology-17657859",
"title": "Walesin myrskytuulien vuoksi annettu sรครคvaroitus",
"summary": "Tuulet voivat yltyรค Walesissa myrskytuuliin, ja myrskysรครค on luvassa koko maahan tรคllรค viikolla.",
"text": "Met Office on antanut Walesin ja Englannin kattavan keltaisen tuulivaroituksen keskiviikkoillasta kello 21.00 GMT alkaen. Matkustaminen ja sรคhkรถnjakelu todennรคkรถisesti hรคiriintyvรคt, ja varoitus on voimassa torstaihin kello 15:00 asti. Puuskat ovat todennรคkรถisesti nopeudeltaan 88 kilometriรค tunnissa, ja rannikoilla ja kukkuloilla puuskat voivat nousta jopa 70 kilometriin tunnissa, ja lisรคksi voi esiintyรค rankkasateita ja myrskyisiรค sadekuuroja."
}
```
### Data Fields
- 'id': A string representing the article ID, matched to the XLSum dataset original
- 'url': A string representing the article URL as in the original XLSum dataset
- 'title': A string containing the article title, machine-translated to Finnish
- 'summary': A string containing the article summary, machine-translated to Finnish
- 'text' : A string containing the article text, machine-translated to Finnish
### Data Splits
Follows the XLSum dataset.
## Dataset Creation
### Curation Rationale
### Source Data
[BBC News](https://www.bbc.co.uk/ws/languages)
#### Initial Data Collection and Normalization
[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/) For this present dataset, only English was used as the source and only examples where the full text is at maximum 10x in length compared to the summary are preserved. This 10x cutoff is naturally measured on English.
#### Who are the source language producers?
[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/)
### Annotations
[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/) DeepL was used to machine-translate from English to Finnish
#### Annotation process
[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/)
#### Who are the annotators?
[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/xl-sum)
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Due to DeepL terms and conditions, this dataset **must not be used for any machine translation work**, namely machine translation system development and evaluation of any kind. In general, we wish you do not pair the original English data with the translations except when working on research unrelated to machine translation, so as not to infringe on the terms and conditions.
## Additional Information
### Dataset Curators
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use any of the datasets, models or code modules, please cite the original XL-Sum paper below as well as acknowledge Filip Ginter and the TurkuNLP group for the Finnish machine translated version.
```
@inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.413",
pages = "4693--4703",
}
```
### Contributions
Thanks to the creators of the XLSum dataset! | TurkuNLP/xlsum-fi | [
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:found",
"language_creators:machine translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:xlsum",
"language:fi",
"license:cc-by-nc-sa-4.0",
"conditional-text-generation",
"region:us"
] | 2022-09-30T12:10:05+00:00 | {"annotations_creators": ["found"], "language_creators": ["machine translated"], "language": ["fi"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["xlsum"], "task_categories": ["summarization", "text2text-generation"], "task_ids": [], "pretty_name": "XL-Sum-FI", "tags": ["conditional-text-generation"]} | 2022-10-25T05:30:19+00:00 |
5e63d4fc3c1140553c27f8db01e881011147b0b6 | This dataset was pushed to Hub through the UI. | Besedo/random-dataset-10000 | [
"region:us"
] | 2022-09-30T12:36:11+00:00 | {} | 2022-09-30T14:27:40+00:00 |
882bcea9e7a2a6c83e55fee2f9021b4bdf4f95f2 | This dataset was programmatically uploaded to this repo using huggingface-hub Python API | Besedo/random-dataset-1000000 | [
"region:us"
] | 2022-09-30T12:55:38+00:00 | {} | 2022-09-30T14:25:51+00:00 |
f7acf28f3bc22c988e42df0d69e72eadd1efe329 | Marcelpribu/stabledifusion | [
"license:other",
"region:us"
] | 2022-09-30T13:05:54+00:00 | {"license": "other"} | 2023-07-06T15:48:39+00:00 |
|
27624246741bea210f5f437820169dc2e39d41d4 |
# Dataset Card for MSMARCO - Natural Language Generation Task
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://microsoft.github.io/msmarco/
- **Repository:** https://github.com/microsoft/MSMARCO-Question-Answering
- **Paper:** https://arxiv.org/abs/1611.09268
- **Leaderboard:** https://microsoft.github.io/msmarco#qnadataset
### Dataset Summary
The original focus of MSMARCO was to provide a corpus for training and testing systems which given a real domain user query systems would then provide the most likley candidate answer and do so in language which was natural and conversational. All questions have been generated from real anonymized Bing user queries which grounds the dataset in a real world problem and can provide researchers real contrainsts their models might be used in. The context passages, from which the answers in the dataset are derived, are extracted from real web documents using the most advanced version of the Bing search engine. The answers to the queries are human generated.
### Supported Tasks and Leaderboards
Question Answering & Natural Language Generation. [Leaderboard](https://microsoft.github.io/msmarco#qnadataset)
### Languages
- English
## Dataset Structure
### Data Instances
```py
{
"query_id":604568,
"query":"what county is columbus city in",
"passages":[
{
"is_selected":0,
"passage_text":"WELCOME TO COLUMBUS! The City of Columbus includes a mix of residential, rural and commercial property. Columbus boasts large tracts of public land, including Carlos Avery Wildlife Management Area and Lamprey Pass.",
"url":"http://www.ci.columbus.mn.us/"
},
{
"is_selected":0,
"passage_text":"The ratio of number of residents in Columbus to the number of sex offenders is 488 to 1. The number of registered sex offenders compared to the number of residents in this city is near the state average. Nearest city with pop. 50,000+: Bloomington, IN (33.3 miles , pop. 69,291).",
"url":"http://www.city-data.com/city/Columbus-Indiana.html"
},
{
"is_selected":0,
"passage_text":"Phone Number: Columbus-Muscogee, the first consolidated city-county in Georgia, began development in 1826, building on ceded Creek Indian territory. Muscogee is the name of a branch of the Creek Nation. Columbus, of course, is named for Christopher Columbus.",
"url":"https://georgia.gov/cities-counties/columbus-muscogee-county"
},
{
"is_selected":1,
"passage_text":"Sponsored Topics. Columbus ( /kษlสmbษs/) is a city in and the county seat of Bartholomew County, Indiana, United States. The population was 44,061 at the 2010 census, and the current mayor is Fred Armstrong. Located approximately 40 miles (64 km) south of Indianapolis, on the east fork of the White River, it is the state's 20th largest city.",
"url":"https://www.mapquest.com/us/in/columbus-282032817"
},
{
"is_selected":0,
"passage_text":"Columbus, Ohio. Columbus (/kษหlสmbษs/; kษ-LUM-bษs) is the capital and largest city of the U.S. state of Ohio. It is the 15th-largest city in the United States, with a population of 850,106 as of 2015 estimates. This makes Columbus the fourth-most populous state capital in the United States, and the third-largest city in the Midwestern United States.",
"url":"https://en.wikipedia.org/wiki/Columbus,_Ohio"
},
{
"is_selected":0,
"passage_text":"Phone Number: Columbus-Muscogee, the first consolidated city-county in Georgia, began development in 1826, building on ceded Creek Indian territory. Muscogee is the name of a branch of the Creek Nation. Columbus, of course, is named for Christopher Columbus.",
"url":"https://georgia.gov/cities-counties/columbus"
},
{
"is_selected":0,
"passage_text":"Latest news from Columbus, IN collected exclusively by city-data.com from local newspapers, TV, and radio stations. Ancestries: American (30.5%), German (13.7%), English (7.7%), Irish (5.3%), European (2.4%), Scottish (1.2%).",
"url":"http://www.city-data.com/city/Columbus-Indiana.html"
},
{
"is_selected":0,
"passage_text":"Columbus, Indiana. 1 Columbus: covered Bridge at Mill Race Park. 2 Columbus: A statue in cloumbus. 3 Columbus. Columbus: Bartholomew County Courthouse. Columbus: Tipton Lakes - A wonderful planned 1 community! Columbus: Barthalomew county memorial for veterans. Columbus: A sculpter called summer storm in 1 columbus. Columbus: Downtown Columbus.",
"url":"http://www.city-data.com/city/Columbus-Indiana.html"
},
{
"is_selected":0,
"passage_text":"The City owns and operates a volunteer fire department through a joint powers agreement with the City of Forest Lake. Police protection is provided through a contract with the Anoka County Sheriffโs Department. Columbus is located within the Forest Lake Area School District (ISD #831).",
"url":"http://www.ci.columbus.mn.us/"
},
{
"is_selected":0,
"passage_text":"Acceptable ID for children: State ID, Birth Certificate, or Health Insurance Card. Effective June 27, 2016, the Franklin County Sheriff's Office will be implementing changes to ensure the safety of inmates, staff, and visitors. Printed materials (magazines, books, pamphlets, leaflets, or catalogues) MUST fit all the below criteria:",
"url":"https://sheriff.franklincountyohio.gov/services/inmate-information.cfm"
}
],
"query_type":"LOCATION",
"answers":[
"Columbus is a city in Bartholomew County."
]
}
```
### Data Fields
- `query_id`: a unique id for each query that is used in evaluation
- `query`: a unique query based on initial Bing usage
- `passages`: a list of 10 passages (`passage_text`), URLs (`url`), and an annotation if they were used to formulate the answer (`is_selected`)
- `query_type`: a basic division of queries based on a trained classifier (`LOCATION`,`NUMERIC`,`PERSON`,`DESCRIPTION`,`ENTITY`)
- `answers`: a list of "well-formed" answers generated by human annotators using natural language
### Data Splits
| **Split** | **Instances** |
|-----------|---------------|
| Train | 153725 |
| Dev | 12467 |
## Dataset Creation
### Curation Rationale
What is the differences between MSMARCO and other MRC datasets?
- Real questions: All questions have been sampled from real anonymized bing queries.
- Real Documents: Most of the URLs that the passages were sourced from contain the full web documents (passages).
- Human Generated Well-Formed Answers: All questions have an answer written by a human in natural language.
### Annotations
#### Annotation process
The MSMARCO dataset is generated by a well oiled pipeline optimized for the highest quality examples. The general process runs as follows:
1. Bing logs are sampled, filtered and anonymized to make sure the queries are both useful to the research community and respectful to bing users and fans.
2. Using the sampled and anonymized queries Bing generates the 10 most relevant passages for the query.
3. Highly trained judges read the query and its related passages and if there is an answer present, the supporting passages are annotated and a natural language answer is generated.
4. A smaller proportion of queries(~17% of overall dataset with 182,887 unique queries) are then passed on to a second round of judges who are asked to verify the answer is correct and rewrite(if possible) the query to be a well formed answer. These answers are designed to be understood without perfect context and are designed with smart speakers/digital assistants in mind.
## Additional Information
### Licensing Information
MS MARCO is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
booktitle={InCoCo@NIPS},
year={2016}
}
```
### Contributions
Thanks to [@din0s](https://github.com/din0s) for adding this dataset. | din0s/msmarco-nlgen | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|ms_marco",
"language:en",
"license:cc-by-4.0",
"msmarco",
"natural language generation",
"question answering",
"arxiv:1611.09268",
"region:us"
] | 2022-09-30T13:06:45+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|ms_marco"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "pretty_name": "MSMARCO NLGEN", "tags": ["msmarco", "natural language generation", "question answering"]} | 2022-10-01T11:30:18+00:00 |
286c357e880bb89dabca70d160a43860517f875b | DrHalom/hk-grey | [
"license:afl-3.0",
"region:us"
] | 2022-09-30T14:10:47+00:00 | {"license": "afl-3.0"} | 2022-09-30T14:21:38+00:00 |
|
53bcd56b0b84c8556802f98916fd2ab63b048af8 | DavLeonardo/sofi | [
"region:us"
] | 2022-09-30T14:27:50+00:00 | {} | 2022-09-30T21:37:23+00:00 |
|
65fe91d79e2e3360048c78eae0905634cf57bb99 | DavLeonardo/fotitos | [
"region:us"
] | 2022-09-30T14:31:45+00:00 | {} | 2022-09-30T15:58:50+00:00 |
|
93f49f7324347fc8ea13e5d6ff99de978292b293 | Alexvval/alexvalval | [
"license:cc",
"region:us"
] | 2022-09-30T15:59:17+00:00 | {"license": "cc"} | 2022-09-30T16:22:36+00:00 |
|
5e3ddde521c24727a134e4825d2927de25784c41 |
# Dataset Card for Lipogram-e
## 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/Hellisotherpeople/Constrained-Text-Generation-Studio
- **Repository**: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio
- **Paper** Most Language Models can be Poets too: An AI Writing Assistant
and Constrained Text Generation Studio
- **Leaderboard**: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio
- **Point of Contact**: https://www.linkedin.com/in/allen-roush-27721011b/
### Dataset Summary
This is a dataset of 3 English books which do not contain the letter "e" in them. This dataset includes all of "Gadsby" by Ernest Vincent Wright, all of "A Void" by Georges Perec, and almost all of "Eunoia" by Christian Bok (except for the single chapter that uses the letter "e" in it)
This dataset is contributed as part of a paper titled "Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio" to appear at COLING 2022.
This dataset and the works within them are examples of Lipograms, which are works where a letter or string is systematically omitted. Lipograms are an example of hard-constrained writing.
### Supported Tasks and Leaderboards
The main task for this dataset is Constrained Text Generation - but all types of language modeling are suitable.
### Languages
English
## Dataset Structure
### Data Instances
Each is extracted directly from the available pdf or epub documents converted to txt using pandoc.
### Data Fields
Text. The name of each work appears before the work starts and again at the end, so the books can be trivially split again if necessary.
### Data Splits
None given. The way I do so in the paper is to extract the final 20% of each book, and concatenate these together. This may not be the most ideal way to do a train/test split, but I couldn't think of a better way. I did not believe randomly sampling was appropriate, but I could be wrong.
## Dataset Creation
### Curation Rationale
One way that we could extract text from datasets that doesn't use the letter "e" in it would be to simply computationally parse through large existing datasets for blocks or sentences which don't have the letter "e" in them. Unfortunately, this is extremely unlikely to lead to coherent or meaningful text. Doing so over increasingly large blocks or spans is likely to result in fewer and fewer examples. While the preparation of such a dataset would be fascinating in its own right - it is more interesting from the perspective of fine-tuning language models to have large scale prose narratives which fulfill the given constraint. This constraint of omitting the letter "e" is attractive because several book length works exist which do this.
### Source Data
#### Initial Data Collection and Normalization
Project Gutenberg
#### Who are the source language producers?
Ernest Vincent Wright
Georges Perec
Christian Bok
### Annotations
#### Annotation process
None
#### Who are the annotators?
n/a
### Personal and Sensitive Information
None
## Considerations for Using the Data
There may be conversion artifacts. I noticed 3 cases of the letter "e" being hallucinated from the pdf conversion of "a void" that I had to fix manually. They were reading special characters as the letter "e", and were not due to the authors making mistakes themselves. This implies that at least a few OCR errors exist.
### Social Impact of Dataset
These books have existed for a awhile now, so it's unlikely that this will have dramatic Social Impact.
### Discussion of Biases
This dataset is 100% biased against the letter "e". There may be biases present in contents of these works. It's recommended to read the books before using this in any non research application to verify that they are not problematic.
### Other Known Limitations
It's possible that more works exist but were not well known enough for the authors to find them and include them. Finding such inclusions would be grounds for iteration of this dataset (e.g. a version 1.1 would be released). The goal of this project is to eventually encompass all book length english language "e" lipograms.
## Additional Information
n/a
### Dataset Curators
Allen Roush
### Licensing Information
MIT
### Citation Information
TBA
### Contributions
Thanks to [@Hellisotherpeople](https://github.com/Hellisotherpeople) for adding this dataset.
| Hellisotherpeople/Lipogram-e | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:mit",
"ctgs",
"CTGS",
"constrained-text-generation",
"lipogram",
"i-hate-the-letter-e",
"region:us"
] | 2022-09-30T16:04:19+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "Lipogram-e from Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio", "tags": ["ctgs", "CTGS", "constrained-text-generation", "lipogram", "i-hate-the-letter-e"]} | 2022-09-30T17:04:43+00:00 |
4827f9e34855e68a8001a2970110d4999ac4488c | Freemanvk1/debbie | [
"region:us"
] | 2022-09-30T17:27:18+00:00 | {} | 2022-10-01T06:41:44+00:00 |
|
491755a196fee0a932d93cfa390809f2aeb616d1 | Freemanvk1/Debbie1 | [
"region:us"
] | 2022-09-30T18:40:33+00:00 | {} | 2022-09-30T19:01:36+00:00 |
|
3ff14c818bc168cb674b5f954b3933fc05f55e50 | Kasuzu/522 | [
"license:unknown",
"region:us"
] | 2022-09-30T20:19:14+00:00 | {"license": "unknown"} | 2022-09-30T20:23:27+00:00 |
|
d3264617542ec95d20eab292cb2b227beacc3c53 | # Dataset Card for "lener_br_finetuning_language_model"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | luciolrv/lener_br_finetuning_language_model | [
"region:us"
] | 2022-09-30T20:46:00+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1544086, "num_examples": 2659}, {"name": "validation", "num_bytes": 284559, "num_examples": 665}], "download_size": 1013297, "dataset_size": 1828645}} | 2023-06-11T14:57:37+00:00 |
88bec961bc2ca02d0a760243e6e95552ebc4402d | SantiCalde/santi | [
"license:unknown",
"region:us"
] | 2022-09-30T21:01:44+00:00 | {"license": "unknown"} | 2022-09-30T21:57:28+00:00 |
|
80768cc6b5ab2f7f6f31de1e0cb92c069fc90f34 | PCScreen/ThomazJunior1 | [
"license:unknown",
"region:us"
] | 2022-09-30T21:07:02+00:00 | {"license": "unknown"} | 2022-09-30T21:07:02+00:00 |
|
c797997d442273e284644de093e2e4ff9419632a |
# Dataset Card for "lmqg/qg_frquad"
***IMPORTANT***: This is a dummy dataset for [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad). The original FRQuAD requires to fill a form (https://fquad.illuin.tech/) to get the data, and our lmqg/qg_frquad follows FQuAD's license. If you need lmqg/qg_frquad, please first request the access to FQuAD on their website https://fquad.illuin.tech/ . Once you obtain the access, we will add you to our lmqg group so that you can access https://huggingface.co/datasets/lmqg/qg_frquad.
Leave a comment to the [discussion page](https://huggingface.co/datasets/lmqg/qg_frquad_dummy/discussions/1) to request access to the `lmqg/qg_frquad` after being granted FQuAD access!
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
This is a modified version of [FQuAD](https://huggingface.co/datasets/fquad) for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set.
***IMPORTANT NOTE:*** The license of this dataset belongs to [FQuAD](https://fquad.illuin.tech/), so please check the guideline there and request the right to access the dataset [here](https://fquad.illuin.tech/) promptly if you use the datset.
### Supported Tasks and Leaderboards
* `question-generation`: The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
French (fr)
## Dataset Structure
An example of 'train' looks as follows.
```
{
'answer': '16 janvier 1377',
'question': 'Quand est-ce que Grรฉgoire XI arrive ร Rome ?',
'sentence': "Le pape poursuit son voyage jusqu'ร Rome en passant par Corneto oรน il parvient le 6 dรฉcembre 1376, puis il arrive ร Rome le 16 janvier 1377 en remontant le Tibre.",
'paragraph': "Quant ร Catherine, elle part par voie terrestre en passant par Saint-Tropez, Varazze, puis Gรชnes. C'est dans cette derniรจre ville que, selon la Legenda minore, elle aurait de nouveau rencontrรฉ Grรฉgoire XI. Le pape poursuit son voyage jusqu'ร Rome en passant par Corneto oรน il parvient le 6 dรฉcembre 1376, puis il arrive ร Rome le 16 janvier 1377 en remontant le Tibre.",
'sentence_answer': "Le pape poursuit son voyage jusqu'ร Rome en passant par Corneto oรน il parvient le 6 dรฉcembre 1376, puis il arrive ร Rome le <hl> 16 janvier 1377 <hl> en remontant le Tibre.",
'paragraph_answer': "Quant ร Catherine, elle part par voie terrestre en passant par Saint-Tropez, Varazze, puis Gรชnes. C'est dans cette derniรจre ville que, selon la Legenda minore, elle aurait de nouveau rencontrรฉ Grรฉgoire XI. Le pape poursuit son voyage jusqu'ร Rome en passant par Corneto oรน il parvient le 6 dรฉcembre 1376, puis il arrive ร Rome le <hl> 16 janvier 1377 <hl> en remontant le Tibre.",
'paragraph_sentence': "Quant ร Catherine, elle part par voie terrestre en passant par Saint-Tropez, Varazze, puis Gรชnes. C'est dans cette derniรจre ville que, selon la Legenda minore, elle aurait de nouveau rencontrรฉ Grรฉgoire XI. <hl> Le pape poursuit son voyage jusqu'ร Rome en passant par Corneto oรน il parvient le 6 dรฉcembre 1376, puis il arrive ร Rome le 16 janvier 1377 en remontant le Tibre. <hl>"
}
```
The data fields are the same among all splits.
- `question`: a `string` feature.
- `paragraph`: a `string` feature.
- `answer`: a `string` feature.
- `sentence`: a `string` feature.
- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
`paragraph_sentence` feature is for sentence-aware question generation.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|17543| 3188 |3188 |
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
``` | lmqg/qg_frquad_dummy | [
"task_categories:text2text-generation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:fquad",
"language:fr",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2022-09-30T22:10:39+00:00 | {"language": "fr", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": "10K<n<100K", "source_datasets": "fquad", "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "FQuAD for question generation", "tags": ["question-generation"]} | 2022-11-05T03:05:12+00:00 |
4e2cf183771adae09efa45c170d17d5b04ccdb49 | Dannyseeu/test | [
"license:afl-3.0",
"region:us"
] | 2022-09-30T23:11:10+00:00 | {"license": "afl-3.0"} | 2022-09-30T23:11:10+00:00 |
|
5631a9bd17a096bab2cd02ea23adbf2327db0d91 | # namu.wiki database dump
##
https://namu.wiki/ database dump 2022/03/01<br/>
- 867024 rows
- download size: 3GB
## Usage
```bash
pip install datasets
```
```python
from datasets import load_dataset
dataset = load_dataset("heegyu/namuwiki")
print(dataset["train"][0])
```
```
{'title': '!!์์!!',
'text': '\n[๋ชฉ์ฐจ]\n\n\'\'\'{{{+1 ๏ผ๏ผใใใฃใจ๏ผ๏ผ}}}\'\'\'\n\n== ๊ฐ์ ==\n[[ํ์ผ:3444050440.jpg|width=60%]]\nโฒ[[์ ์ธ๊ณ์์ ๋ฏธ๊ถ 2 ํํ๋๋ฅด๊ธฐ์ฌ|์ ์ธ๊ณ์์ ๋ฏธ๊ถ 2]]์์ ๋ฌ !!์์!!\n\n[[์ธ๊ณ์์ ๋ฏธ๊ถ ์๋ฆฌ์ฆ]]์ ์ ํต์ผ๋ก ๋ฑ์ฅํ๋ ๋์ฌ. [[์ธ๊ณ์์ ๋ฏธ๊ถ 2 ์ ์์ ์ฑ๋ฐฐ|2ํธ]]๋ถํฐ ๋ฑ์ฅํ์ผ๋ฉฐ ํ๋ฅญํ [[์ฌ๋ง ํ๋๊ทธ]]์ ์์์ด๋ค.\n\n์ธ๊ณ์์ ๋ชจํ๊ฐ๋ค์ด ํํํ๋ ๋์ ์ธ ์ํด์ ๊ตฌ์๊ตฌ์์๋ ์ฑ์ทจ/๋ฒ์ฑ/์ฑ๊ตด ํฌ์ธํธ๊ฐ ์์ผ๋ฉฐ, ์ด๋ฅผ ์ํ ์ฑ์ง ์คํฌ์ ํฌ์ํ๋ฉด ์ ํ๋ ์ฑ์ง ๊ธฐํ์์ ๋ณด๋ค ํฐ ์ด๋์ ์ฑ๊ธธ ์ ์๋ค. ๊ทธ๋ฌ๋ ๋ถ๋ฐฐํ ์ ์๋ ์คํฌ ํฌ์ธํธ๋ ํ์ ๋์ด ์๊ธฐ ๋๋ฌธ์ ์ฑ์ง ์คํฌ์ ํฌ์ํ๋ ๋งํผ ์ ํฌ ์คํฌ ๋ ๋ฒจ์ ๋ฎ์์ง๊ฒ ๋๋ค.[* ๋ค๋ง ์ฑ์ง ์์คํ
์ ์ ์ธ๊ณ์ ์๋ฆฌ์ฆ์ ๊ทธ๋ฆฌ๋ชจ์ด ๋ณต์ , ๋ณตํฉ ์ฑ์ง ์คํฌ์ธ ์ผ์์ ๊ฐ, 5ํธ์ ์ข
์กฑ ํน์ ์คํฌ, ํฌ๋ก์ค์ 1๋ ๋ฒจ์ด ๋ง๋ ์ธ ์ฑ์ง ์คํฌ ๋ฑ์ผ๋ก ํธ์์ฑ์ด ์ ์ฐจ ๋์์ ธ์ ์ฑ์ง ์คํฌ ๋๋ฌธ์ ์คํฌ ํธ๋ฆฌ๊ฐ ๋ด๋ ค๊ฐ๋ ์ผ์ ์ ์ ์ค์ด๋ค์๋ค.] !!์์!!์ด ๋ฐ์ํ๋ ๊ณผ์ ์ ์์ฝํ๋ฉด ๋ค์๊ณผ ๊ฐ๋ค.\n\n 1. ์ฑ์ง์ฉ ์บ๋ฆญํฐ๋ค๋ก ์ด๋ฃจ์ด์ง ์ฝํ ํํฐ(ex: [[๋ ์ธ์ (์ธ๊ณ์์ ๋ฏธ๊ถ 2)|๋ ์ธ์ ]] 5๋ช
)๊ฐ ์ํด์ ์
์ฅํ๋ค.\n 1. ํ๋ ์ ํฌ๋ฅผ ํผํด ์ฑ์ง ํฌ์ธํธ์ ๋์ฐฉํ ํ ์ด์ฌํ ์์ดํ
์ ์บ๋ ์ค์...\n 1. \'\'\'!!์์!!\'\'\' ~~๋ผํ๋ ์์๊ฐ ๋ํ๋ฌ๋ค!~~\n ์ด๋ ๋ฑ์ฅํ๋ ๊ฒ์ [[FOE(์ธ๊ณ์์ ๋ฏธ๊ถ ์๋ฆฌ์ฆ)|FOE]]๋ ์๋์ง๋ง \'\'\'ํจ์ฌ ์์ธต์ ๋ฑ์ฅํ๋ ๊ฐ๋ ฅํ ํ๋ ๋ชฌ์คํฐ์ด๋ฉฐ ์ ์ ๊ณต๊ฒฉ์ ๋นํ๊ฒ ๋๋ค!\'\'\'\n 1. \'\'\'์ผ์ ์ฃฝ์\'\'\'(hage)\n\n์ฌ๋ด์ผ๋ก !!์์!!์ ์ ๋๋ 1์ธ์นญ ๋์ ํฌ๋กค๋ฌ์ ์์กฐ [[์์ ๋๋ฆฌ]]์์ ํจ์ ์ ๊ฑด๋๋ ธ์ ๋ ๋์ค๋ ๋์ฌ Oops!(ใใใฃใจ๏ผ)๋ผ๊ณ ํ๋ค.\n\n== ๊ฐ ์ํ์์์ ๋ชจ์ต ==\n=== [[์ธ๊ณ์์ ๋ฏธ๊ถ 2 ์ ์์ ์ฑ๋ฐฐ]] ===\n!!์์!!์ ์
๋ํจ์ ์ฒซ ๋ฑ์ฅํ ์ํ์ด์ ์๋ฆฌ์ฆ ์ค์์๋ ๋ถ์น์ ํ๊ธฐ๋ก ์ ํ์ด ๋ 2ํธ์ด ์ ์ ์ด์๋ค. ๊ทธ์ผ๋ง๋ก ์์ !!์์!! ์ํ์ค ๊ทธ๋๋ก, ๋ฌป์ง๋ ๋ฐ์ง์ง๋ ์๊ณ ์ฑ์งํ ๋๋ง๋ค ์ผ์ ํ๋ฅ ๋ก \'\'\'๊ฐ์ ๋ก\'\'\' ์ ํฌ์ ๋์
ํด์ผ ํ๋ค. ๊ฒ๋ค๊ฐ ์ด๋ด ๋ ์ฐ๋ผ๊ณ ์๋ ๋ ์ธ์ ์ ์คํฌ \'์ํ ๊ฐ์ง(์ค๊ฐ ํ๋ฅ ๋ก ์ ์ ์ ์ ๊ณต๊ฒฉ์ ๋ฌดํจํ)\'๋ ์ ์ ์๋ํ์ง ์๋๋ค!\n\n์ฐธ๊ณ ๋ก 2ํธ์์ ์ฑ์ง ๋์ค !!์์!!์ด ๋ฐ ํ๋ฅ ์ [[http://www.atlusnet.jp/topic/detail/910|๊ณ ์ 1%๋ค.]] [[๋ํํ๋ฅ ์ ๋ฒ์น|๋ฎ์ ๋ณด์ด๋ ํ๋ฅ ์ด์ด๋ ํ๋ ์ด ์ค ํ ๋ฒ์ด๋ผ๋ ์ผ์ด๋๋ ๊ฒ]]์ ๊ฒฝํํ๋ ์ฒด๊ฐ ํ๋ฅ ์ ๊ณ ๋ คํ์ฌ ํ๋ฅ ์ ์ค์ ํ๋ค๊ณ .\n\n=== [[์ธ๊ณ์์ ๋ฏธ๊ถ 3 ์ฑํด์ ๋ด๋ฐฉ์]] ===\n๋คํํ ์ฑ์ง ์ค ๋ฎ์ ํ๋ฅ ๋ก "์ข์ ์์ดํ
์ ์ป์ ์ ์์ ๊ฒ ๊ฐ์ง๋ง... ์ฃผ๋ณ์์ ๋ชฌ์คํฐ๋ค์ ๊ธฐ์ฒ์ด ๋๊ปด์ง๋ค."๋ ๋ฉ์์ง๊ฐ ๋จ๊ณ ์ด๋ ์ด์ด ์ข์ผ๋ฉด ๋ ์ด ์์ดํ
์ ์ป์ ์ ์์ง๋ง ๋ฐ๋์ ๊ฒฝ์ฐ ์ ๊ณผ ์ธ์ฐ๊ฒ ๋๋ ๊ฒ์ผ๋ก ์กฐ์ ๋์๋ค.\n\n=== [[์ธ๊ณ์์ ๋ฏธ๊ถ 4 ์ ์น์ ๊ฑฐ์ ]] ===\n๊ธฐ๋ณธ์ ์ธ ๊ฒ์ 3ํธ๊ณผ ๊ฐ์ง๋ง, 4ํธ์์๋ ์์ง์ด์ง ์๊ณ ์ฑ์งํ ๋๋ ํด์ด ๊ฒฝ๊ณผํ๋๋ก ์กฐ์ ๋์๊ธฐ ๋๋ฌธ์ ์ฃผ๋ณ์ ์๋ FOE๋ฅผ ์๊ณ ์ฑ์ง์ ๋ชฐ๋ํ๋ค๊ฐ FOE์ ๋ถ๋ชํ๋ฉด FOE ๋ฒ์ !!์์!!์ด ๋ฌ๋ค. ๊ทธ๋ฆฌ๊ณ ๋์ด๋ CASUAL๋ก ํ๋ ์ด์, FOE๋ก ์ธํ !!์์!!์ ์ ์ธํ๋ฉด ์ ๋๋ก ๋ฐ์ํ์ง ์๋๋ค.\n\n=== [[์ ์ธ๊ณ์์ ๋ฏธ๊ถ ๋ฐ๋ ๋์์ ์๋
|์ ์ธ๊ณ์์]] [[์ ์ธ๊ณ์์ ๋ฏธ๊ถ 2 ํํ๋๋ฅด๊ธฐ์ฌ|๋ฏธ๊ถ ์๋ฆฌ์ฆ]] ===\n์ฑ์ง ๋ฐฉ์์ด ํ ํด์ผ๋ก ๋๋๋ ๊ตฌ์กฐ[* ์ฑ์ง์ผ๋ก ํ ๋ฒ ์์ดํ
์ ํ๋ํ๋ฉด "๋ค์, (์ฑ์ง ์คํฌ)์ ์ํด..."๊ฐ ๋จ๋ฉด์ ํ๊บผ๋ฒ์ ํ๋๋๋ ๊ตฌ์กฐ.]๋ก ๋ฐ๋ ๋๋ถ์ธ์ง ๊ฐ์ ์กฐ์ฐ๋ก ๋ค์ ํ๊ทํด๋ฒ๋ ธ๋ค(...). ๊ทธ๋๋ง ์ํ ๊ฐ์ง ๋จนํต๊ณผ ๊ฐ์ ๋ฒ๊ทธ์ฑ ๋์ ๋ค์ ์์ ๋์๋ค. ๊ทธ ์ดํ์ ๋์จ [[์ธ๊ณ์์ ๋ฏธ๊ถ 5 ์ค๋ ์ ํ์ ๋]]๊ณผ ์๋ฆฌ์ฆ์ ์ง๋์ฑ ์ํ์ด์ 3DS ๋ง์ง๋ง ์ํ์ธ [[์ธ๊ณ์์ ๋ฏธ๊ถ X]]๋ ๋ง์ฐฌ๊ฐ์ง.\n\n=== [[์ธ๊ณ์์ ๋ฏธ๊ถ X]] ===\n๋ณธ์์ ์ฑ์ง์ ์ ์ธ๊ณ์ ์๋ฆฌ์ฆ์ ๊ฐ์ ๋งค์ปค๋์ฆ์ด๋ผ ๊ตณ์ด ์ธ๊ธํ ํ์๋ ์์ผ๋, ํ์คํธ์ค์ 2ํธ์ !!์์!! ์ํ์ค๋ฅผ ์ฌํํ๋ฉด์ \'\'\'๋ผํ๋ ์์\'\'\'๊ฐ ๋ฑ์ฅํ๋ ํ์คํธ๊ฐ ์กด์ฌํ๋ค.(...) ๊นจ์๊ฐ์ด ์์คํ
๋ฉ์ธ์ง ์ฐฝ์ด ์๋๋ผ ๋ํ์ฐฝ์ ์ด์ฉํด์ ์๋ฒฝ ์ฌํํ ๊ฒ์ด ํฌ์ธํธ.\n\n=== [[ํ๋ฅด์๋ Q ์๋์ฐ ์ค๋ธ ๋ ๋๋ฒ๋ฆฐ์ค]] ===\n์ธ๊ณ์ ์์คํ
์ ๊ธฐ๋ฐ์ผ๋ก ํ [[ํ๋ฅด์๋ ์๋ฆฌ์ฆ]]์์ ์ฝ๋ผ๋ณด ์ํ์ธ ํ๋ฅด์๋ Q์์๋ ๋ฑ์ฅํ๋ค. 3, 4ํธ๊ณผ ๊ฐ์ด ํ์ ์คํฟ์์ ์ฑ์ง ๋์ค ๋ฉ์์ง๊ฐ ๋จ๋ฉฐ, ์คํจํ๋ฉด ํํฐ์ ์ฐธ๊ฐํ๊ณ ์๋ ๋ฉค๋ฒ ์ค ํ ๋ช
์ [[http://nico.ms/sm25683358|!!์์!! ํ๋ ์์ฑ]] ~~๋๋ [[์ฝ๋ก๋ง๋ฃจ|๊ฐ์๋ฆฌ]]~~๊ณผ ํจ๊ป ๊ทธ ๋์ ์ \'๊ฐ์ \'์ธ ๊ฑฐ๋ [[์๋(ํ๋ฅด์๋ ์๋ฆฌ์ฆ)|์๋์ฐ]]๊ฐ ๋ํ๋๋ค.\n\n๊ทธ๋ฌ๋ ๋ด๋น ์ ์ฉ ์คํฌ์ธ ๋ฑ๋ ๋
ธ๋ ค๋ณด๊ธฐ(์ํ ๊ฐ์ง์ ๊ฐ์ ํจ๊ณผ)์ ์ฑ์ง ๋ณด์กฐ ์คํฌ์ ํํฐ์ ์ ํฌ๋ ฅ์ ์ ํ ์ง์ฅ์ ์ฃผ์ง ์์ผ๋ฉฐ, \'๋์์ฌ\'์ ๋ฌ๋ฉด ๊ฑฐ์ ๋ณผ ์ผ์ด ์์ด์ ธ์ ์ด์ค๋ฐ ์ดํ์๋ ์กด์ฌ๊ฐ์ด ๊ธ๊ฒฉํ ์ค์ด๋ ๋ค.\n[[๋ถ๋ฅ:์ธ๊ณ์์ ๋ฏธ๊ถ ์๋ฆฌ์ฆ]]',
'contributors': '110.46.34.123,kirby10,max0243,218.54.117.149,ruby3141,121.165.63.239,iviyuki,1.229.200.194,anatra95,kiri47,175.127.134.2,nickchaos71,chkong1998,kiwitree2,namubot,huwieblusnow',
'namespace': ''}
``` | heegyu/namuwiki | [
"task_categories:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:ko",
"license:cc-by-nc-sa-2.0",
"region:us"
] | 2022-09-30T23:40:12+00:00 | {"language_creators": ["other"], "language": ["ko"], "license": "cc-by-nc-sa-2.0", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["other"]} | 2022-10-01T01:40:40+00:00 |
d65e67ec2ab80128e746d83e2aaf3888f096e29f | MoreMemes/Image | [
"license:openrail",
"region:us"
] | 2022-10-01T00:03:04+00:00 | {"license": "openrail"} | 2022-10-01T00:14:46+00:00 |
|
d5ef945611040f7f760e02abfdc05be74b01edbe | # namu.wiki database dump
##
https://namu.wiki/ database dump 2022/03/01<br/>
- 571308rows
- download size: 2.19GB
## ์ฃผ์์ฌํญ
namu-wiki-extractor๋ฅผ ์ด์ฉํ์ฌ ์ ์ฒ๋ฆฌ, ์ถ๊ฐ๋ก ์๋ ์ ์ฒ๋ฆฌ๋ฅผ ์ํํ์ต๋๋ค
1. ํค๋ ์ ๊ฑฐ `== ๊ฐ์ ==`
1. ํ
์ด๋ธ ์ ๊ฑฐ
1. `[age(1997-01-01)]` ๋ ์ ์ฒ๋ฆฌ ์์ ๊ธฐ์ค์ผ๋ก ์ ์ฉ(2022๋
10์ 2์ผ)
1. `[math(a / b + c)]` ๋ ์ ๊ฑฐํ์ง ์์.
1. math ๋งํฌ๋ค์ด์ด ๊ฐ์ฃผ ๋ด์ ์์ ๊ฒฝ์ฐ, ๊ฐ์ฃผ๊ฐ ์ ์ฒ๋ฆฌ๋์ง ์์ ๋ฌธ์ ์์.
## Usage
```bash
pip install datasets
```
```python
from datasets import load_dataset
dataset = load_dataset("heegyu/namuwiki-extracted")
print(dataset["train"][0])
```
```
{
'title': '!!์์!!',
'text': '๏ผ๏ผใใใฃใจ๏ผ๏ผ โฒ์ ์ธ๊ณ์์ ๋ฏธ๊ถ 2์์ ๋ฌ !!์์!! ์ธ๊ณ์์ ๋ฏธ๊ถ ์๋ฆฌ์ฆ์ ์ ํต์ผ๋ก ๋ฑ์ฅํ๋ ๋์ฌ. 2ํธ๋ถํฐ ๋ฑ์ฅํ์ผ๋ฉฐ ํ๋ฅญํ ์ฌ๋ง ํ๋๊ทธ์ ์์์ด๋ค. ์ธ๊ณ์์ ๋ชจํ๊ฐ๋ค์ด ํํํ๋ ๋์ ์ธ ์ํด์ ๊ตฌ์๊ตฌ์์๋ ์ฑ์ทจ/๋ฒ์ฑ/์ฑ๊ตด ํฌ์ธํธ๊ฐ ์์ผ๋ฉฐ, ์ด๋ฅผ ์ํ ์ฑ์ง ์คํฌ์ ...',
'contributors': '110.46.34.123,kirby10,max0243,218.54.117.149,ruby3141,121.165.63.239,iviyuki,1.229.200.194,anatra95,kiri47,175.127.134.2,nickchaos71,chkong1998,kiwitree2,namubot,huwieblusnow',
'namespace': ''
}
``` | heegyu/namuwiki-extracted | [
"task_categories:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:ko",
"license:cc-by-nc-sa-2.0",
"region:us"
] | 2022-10-01T00:27:07+00:00 | {"language_creators": ["other"], "language": ["ko"], "license": "cc-by-nc-sa-2.0", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["other"]} | 2023-01-15T09:46:31+00:00 |
45874d64c3667373215855cbdf86611caec69386 | ZidaneAdnie/test | [
"license:afl-3.0",
"region:us"
] | 2022-10-01T02:37:41+00:00 | {"license": "afl-3.0"} | 2022-10-01T02:38:29+00:00 |
|
215e153b7ffc3017223f2a2bc580cf3d27c67bd2 | ali4546/all | [
"license:openrail",
"region:us"
] | 2022-10-01T03:17:50+00:00 | {"license": "openrail"} | 2022-10-01T03:17:50+00:00 |
|
13a03baacde282bc1573bee2963ea0ca677286d3 |
- 38,015,081 rows | heegyu/namuwiki-sentences | [
"task_categories:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:ko",
"license:cc-by-nc-sa-2.0",
"region:us"
] | 2022-10-01T03:48:22+00:00 | {"language_creators": ["other"], "language": ["ko"], "license": "cc-by-nc-sa-2.0", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["other"]} | 2022-10-14T06:55:44+00:00 |
62f2c7c1ccbf16e4a335042a5fad8be287abd525 | yihan422/johnny | [
"license:openrail",
"region:us"
] | 2022-10-01T05:04:55+00:00 | {"license": "openrail"} | 2022-10-01T05:17:11+00:00 |
|
0e661470ee297dc7b3d13fa9e70ff4c9e96cd1a2 | # AutoTrain Dataset for project: ashwin_sentiment140dataset
## Dataset Description
This dataset has been automatically processed by AutoTrain for project ashwin_sentiment140dataset.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "@JordainFTW i didnt watch them BUT CALEB PLAYS NAZI ZOMBIES TOOOOOO!!!!!!!!!! OMG OMG OMG! HE IS MY BESTFREIND! what do u needa tell me?",
"target": 1
},
{
"text": "@Jennymac22 too much info! good for you hun. I'm pleased for you. ",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['0', '4'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2399 |
| valid | 601 |
| ashwinperti/autotrain-data-ashwin_sentiment140dataset | [
"task_categories:text-classification",
"language:en",
"region:us"
] | 2022-10-01T07:39:41+00:00 | {"language": ["en"], "task_categories": ["text-classification"]} | 2022-10-01T07:40:44+00:00 |
62787336499fc5af51407182cb354420f7cdc160 |
# Dataset Card for "IE-SemParse"
## Table of Contents
- [Dataset Card for "IE-SemParse"](#dataset-card-for-ie-semparse)
- [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 usage](#dataset-usage)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Human Verification Process](#human-verification-process)
- [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)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** <https://github.com/divyanshuaggarwal/IE-SemParse>
- **Paper:** [Evaluating Inter-Bilingual Semantic Parsing for Indian Languages](https://arxiv.org/abs/2304.13005)
- **Point of Contact:** [Divyanshu Aggarwal](mailto:[email protected])
### Dataset Summary
IE-SemParse is an InterBilingual Semantic Parsing Dataset for eleven major Indic languages that includes
Assamese (โasโ), Gujarat (โguโ), Kannada (โknโ),
Malayalam (โmlโ), Marathi (โmrโ), Odia (โorโ),
Punjabi (โpaโ), Tamil (โtaโ), Telugu (โteโ), Hindi
(โhiโ), and Bengali (โbnโ).
### Supported Tasks and Leaderboards
**Tasks:** Inter-Bilingual Semantic Parsing
**Leaderboards:** Currently there is no Leaderboard for this dataset.
### Languages
- `Assamese (as)`
- `Bengali (bn)`
- `Gujarati (gu)`
- `Kannada (kn)`
- `Hindi (hi)`
- `Malayalam (ml)`
- `Marathi (mr)`
- `Oriya (or)`
- `Punjabi (pa)`
- `Tamil (ta)`
- `Telugu (te)`
...
<!-- Below is the dataset split given for `hi` dataset.
```python
DatasetDict({
train: Dataset({
features: ['utterance', 'logical form', 'intent'],
num_rows: 36000
})
test: Dataset({
features: ['utterance', 'logical form', 'intent'],
num_rows: 3000
})
validation: Dataset({
features: ['utterance', 'logical form', 'intent'],
num_rows: 1500
})
})
``` -->
## Dataset usage
Code snippet for using the dataset using datasets library.
```python
from datasets import load_dataset
dataset = load_dataset("Divyanshu/IE_SemParse")
```
## Dataset Creation
Machine translation of 3 multilingual semantic Parsing datasets english dataset to 11 listed Indic Languages.
### Curation Rationale
[More information needed]
### Source Data
[mTOP dataset](https://aclanthology.org/2021.eacl-main.257/)
[multilingualTOP dataset](https://github.com/awslabs/multilingual-top)
[multi-ATIS++ dataset](https://paperswithcode.com/paper/end-to-end-slot-alignment-and-recognition-for)
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
#### Human Verification Process
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
## Considerations for Using the Data
### Social Impact of Dataset
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
### Discussion of Biases
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
### Other Known Limitations
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
### Dataset Curators
Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use any of the datasets, models or code modules, please cite the following paper:
```
@misc{aggarwal2023evaluating,
title={Evaluating Inter-Bilingual Semantic Parsing for Indian Languages},
author={Divyanshu Aggarwal and Vivek Gupta and Anoop Kunchukuttan},
year={2023},
eprint={2304.13005},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!-- ### Contributions -->
| Divyanshu/IE_SemParse | [
"task_categories:text2text-generation",
"task_ids:parsing",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc0-1.0",
"arxiv:2304.13005",
"region:us"
] | 2022-10-01T09:51:54+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": ["parsing"], "pretty_name": "IE-SemParse"} | 2023-07-13T17:35:10+00:00 |
3fd0b1c937a8c437c9ff0f4780b58c077fd2b364 | quintend/rdr-items | [
"region:us"
] | 2022-10-01T12:07:46+00:00 | {} | 2022-10-02T10:38:18+00:00 |
|
f221043ea28fcd9a6fddcf46ebde8ebdc74ab667 | Brathna/nanao | [
"license:openrail",
"region:us"
] | 2022-10-01T12:53:22+00:00 | {"license": "openrail"} | 2022-10-01T12:53:22+00:00 |
|
b505c573ccc1c2491f70e3a36672ce75596427ae | Hallucinate/cryptopunks_5_gpt2 | [
"region:us"
] | 2022-10-01T12:53:23+00:00 | {} | 2022-10-01T12:53:59+00:00 |
|
279c76889787d6c2e7f3e259af0f52ffa8fa1626 | MIlgacia/eret | [
"region:us"
] | 2022-10-01T13:08:14+00:00 | {} | 2022-10-13T07:58:19+00:00 |
|
c9d466e0d1ebf538e1e9c666ca6596405e292cad | earroyo/earroyoluna | [
"license:openrail",
"region:us"
] | 2022-10-01T13:21:05+00:00 | {"license": "openrail"} | 2022-10-01T13:22:24+00:00 |
|
41b15060fa8d35740ef0e2dcf908a9b987d51690 | zoiz/test | [
"license:afl-3.0",
"region:us"
] | 2022-10-01T14:08:21+00:00 | {"license": "afl-3.0"} | 2022-10-01T14:09:09+00:00 |
|
338ff07c51b098d242e535cd8d7d536e873dea68 | # Dataset Card for Auditor Sentiment | ihassan1/auditor-sentiment | [
"task_categories:text-classification",
"task_ids:sentiment-scoring",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"auditor",
"financial",
"sentiment",
"markets",
"region:us"
] | 2022-10-01T14:10:00+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": [], "license": [], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["sentiment-scoring"], "pretty_name": "auditor-sentiment", "tags": ["auditor", "financial", "sentiment", "markets"]} | 2022-10-02T07:44:54+00:00 |
6d7bd4418d47bf4a598c577e7e16c59a854d1b90 | earroyo/earroyo | [
"license:openrail",
"region:us"
] | 2022-10-01T15:06:41+00:00 | {"license": "openrail"} | 2022-10-01T15:13:44+00:00 |
|
8f32ed1a4ce202774cc29ed193671366c5b1d85e | Mainred/model | [
"license:unknown",
"region:us"
] | 2022-10-01T15:56:43+00:00 | {"license": "unknown"} | 2022-10-01T18:46:07+00:00 |
|
91da2c2cc70b97d9403d36f620d7f504a5952f7b | MarianaMolina007/NASAART | [
"license:cc",
"region:us"
] | 2022-10-01T16:08:27+00:00 | {"license": "cc"} | 2022-10-02T11:23:55+00:00 |
|
53e379cb1f25191b32d37c43646edade37434e59 | # AutoTrain Dataset for project: oveja31
## Dataset Description
This dataset has been automatically processed by AutoTrain for project oveja31.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1424x1424 RGB PIL image>",
"target": 0
},
{
"image": "<1627x1627 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(num_classes=1, names=['oveja'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 4 |
| valid | 1 |
| freefire31/autotrain-data-oveja31 | [
"task_categories:image-classification",
"region:us"
] | 2022-10-01T16:23:21+00:00 | {"task_categories": ["image-classification"]} | 2022-10-01T16:26:57+00:00 |
c72b2a584cc89b468e1d54759df144dd2d08751f |
# Dataset Card for Lipogram-e
## 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/Hellisotherpeople/Constrained-Text-Generation-Studio
- **Repository**: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio
- **Paper** Most Language Models can be Poets too: An AI Writing Assistant
and Constrained Text Generation Studio
- **Leaderboard**: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio
- **Point of Contact**: https://www.linkedin.com/in/allen-roush-27721011b/
### Dataset Summary
This is a dataset of English books which only write using one syllable at a time. At this time, the dataset only contains Robinson Crusoe โ in Words of One Syllable by Lucy Aikin and Daniel Defoe
This dataset is contributed as part of a paper titled "Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio" to appear at COLING 2022. This dataset does not appear in the paper itself, but was gathered as a candidate constrained text generation dataset.
### Supported Tasks and Leaderboards
The main task for this dataset is Constrained Text Generation - but all types of language modeling are suitable.
### Languages
English
## Dataset Structure
### Data Instances
Each is extracted directly from the available pdf or epub documents converted to txt using pandoc.
### Data Fields
Text. The name of each work appears before the work starts and again at the end, so the books can be trivially split again if necessary.
### Data Splits
None given. The way I do so in the paper is to extract the final 20% of each book, and concatenate these together. This may not be the most ideal way to do a train/test split, but I couldn't think of a better way. I did not believe randomly sampling was appropriate, but I could be wrong.
## Dataset Creation
### Curation Rationale
There are several books which claim to only be written using one syllable words. A list of them can be found here: https://diyhomeschooler.com/2017/01/25/classics-in-words-of-one-syllable-free-ebooks/
Unfortunately, after careful human inspection, it appears that only one of these works actually does reliably maintain the one syllable constraint through the whole text. Outside of proper names, I cannot spot or computationally find a single example of a more-than-one-syllable word in this whole work.
### Source Data
Robinson Crusoe โ in Words of One Syllable by Lucy Aikin and Daniel Defoe
#### Initial Data Collection and Normalization
Project Gutenberg
#### Who are the source language producers?
Lucy Aikin and Daniel Defoe
### Annotations
#### Annotation process
None
#### Who are the annotators?
n/a
### Personal and Sensitive Information
None
## Considerations for Using the Data
There may be OCR conversion artifacts.
### Social Impact of Dataset
These books have existed for a awhile now, so it's unlikely that this will have dramatic Social Impact.
### Discussion of Biases
The only biases possible are related to the contents of Robinson Crusoe or the possibility of the authors changing Robinson Crusoe in some problematic way by using one-syllable words. This is unlikely, as this work was aimed at children.
### Other Known Limitations
It's possible that more works exist but were not well known enough for the authors to find them and include them. Finding such inclusions would be grounds for iteration of this dataset (e.g. a version 1.1 would be released). The goal of this project is to eventually encompass all book length english language works that do not use more than one syllable in each of their words (except for names)
## Additional Information
n/a
### Dataset Curators
Allen Roush
### Licensing Information
MIT
### Citation Information
TBA
### Contributions
Thanks to [@Hellisotherpeople](https://github.com/Hellisotherpeople) for adding this dataset.
| Hellisotherpeople/one_syllable | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"syllable",
"one_syllable",
"region:us"
] | 2022-10-01T16:39:29+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "one_syllable from Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio", "tags": ["syllable", "one_syllable"]} | 2022-10-01T16:46:42+00:00 |
d8867e3859435abc058208e0fe5fda18a621a31e | urbanalaura/toni | [
"license:creativeml-openrail-m",
"region:us"
] | 2022-10-01T17:39:20+00:00 | {"license": "creativeml-openrail-m"} | 2022-10-01T17:41:43+00:00 |
|
4214e4cc24f29f54c25a794aedbf14f4fb8e130b | RAPTORIDK/Face | [
"license:unknown",
"region:us"
] | 2022-10-01T18:11:47+00:00 | {"license": "unknown"} | 2022-10-01T18:46:10+00:00 |
|
e5b5a21dc0e4977078885497e4343e3c27a92278 | jwfeniello/skrill | [
"license:openrail",
"region:us"
] | 2022-10-01T18:39:27+00:00 | {"license": "openrail"} | 2022-10-01T18:39:27+00:00 |
|
a5c4b9c07d04a685a0880cc31df2df24747b935d | ELUNIVERSODEJDC/liuouio | [
"license:openrail",
"region:us"
] | 2022-10-01T19:53:27+00:00 | {"license": "openrail"} | 2022-10-01T19:53:27+00:00 |
|
505c751fbe3932c04c1e85ae1f21b2a460e8c446 | RAPTORIDK/Mi-Cara | [
"license:unknown",
"region:us"
] | 2022-10-01T20:07:17+00:00 | {"license": "unknown"} | 2022-10-01T20:17:01+00:00 |
|
d42e77e41b6370eebff7ae693554e2683927165e | ZeFluffyNuphkin/testimage | [
"region:us"
] | 2022-10-01T22:37:24+00:00 | {} | 2022-10-01T22:37:59+00:00 |
|
dd14ef9eaf8a803cc68cec01f9fc7d353d162264 | ํ๊ตญ์ด ์ํคํผ๋์ article ๋คํ(20221001)
- 1334694 rows
- download size: 474MB
```python
from datasets import load_dataset
ds = load_dataset("heegyu/kowikitext", "20221001")
ds["train"][0]
```
```
{'id': '5',
'revid': '595831',
'url': 'https://ko.wikipedia.org/wiki?curid=5',
'title': '์ง๋ฏธ ์นดํฐ',
'text': '์ ์์ค ์ผ ์นดํฐ ์ฃผ๋์ด(, 1924๋
10์ 1์ผ ~ )๋ ๋ฏผ์ฃผ๋น ์ถ์ ๋ฏธ๊ตญ 39๋ ๋ํต๋ น (1977๋
~ 1981๋
)์ด๋ค.\n์์ .\n์ด๋ฆฐ ์์ .\n์ง๋ฏธ ์นดํฐ๋ ์กฐ์ง์์ฃผ ์ฌํฐ ์นด์ดํฐ ํ๋ ์ธ์ค ๋ง์์์ ํ์ด๋ฌ๋ค.\n์กฐ์ง์ ๊ณต๊ณผ๋ํ๊ต๋ฅผ ์กธ์
ํ์๋ค. ๊ทธ ํ ํด๊ตฐ์ ๋ค์ด๊ฐ ์ ํจยท์์๋ ฅยท์ ์ํจ์ ์น๋ฌด์์ผ๋ก ์ผํ์๋ค. 1953๋
๋ฏธ๊ตญ ํด๊ตฐ ๋์๋ก ์ํธํ์๊ณ ์ดํ ๋
์ฝฉยท๋ฉดํ ๋ฑ์ ๊ฐ๊ฟ ๋ง์ ๋์ ๋ฒ์๋ค. ๊ทธ์ ๋ณ๋ช
์ด "๋
์ฝฉ ๋๋ถ" (Peanut Farmer)๋ก ์๋ ค์ก๋ค.\n์ ๊ณ ์
๋ฌธ.\n1962๋
์กฐ์ง์์ฃผ ์์ ์์ ์ ๊ฑฐ์์ ๋์ ํ๋ ๊ทธ ์ ๊ฑฐ๊ฐ ๋ถ์ ์ ๊ฑฐ ์์์ ... "
}
```
| heegyu/kowikitext | [
"license:cc-by-sa-3.0",
"region:us"
] | 2022-10-02T01:40:05+00:00 | {"license": "cc-by-sa-3.0"} | 2022-10-02T04:07:59+00:00 |
9b1ce6e04596ad6e820bf11d5617a58788b6be8f | pipexta/yo | [
"license:afl-3.0",
"region:us"
] | 2022-10-02T03:59:16+00:00 | {"license": "afl-3.0"} | 2022-10-02T04:09:07+00:00 |
|
c7775ee196a6b7fd3ef1b2d74ee0be731ff1edf5 | 
| halo1998/yo | [
"region:us"
] | 2022-10-02T04:41:14+00:00 | {} | 2022-10-02T04:50:35+00:00 |
061145ef43c2bad28c8c81d9c1d9fb4448c8840b | Smuzzer/Rach | [
"license:openrail",
"region:us"
] | 2022-10-02T06:59:28+00:00 | {"license": "openrail"} | 2022-10-02T07:07:11+00:00 |
|
b281fecc25a04fc100389a93fce9d835bf9ec347 | imagenes logo del real union tenerife
license: other
---
| ricewind/logo-union | [
"region:us"
] | 2022-10-02T09:59:07+00:00 | {} | 2022-10-02T10:13:10+00:00 |
fbcb4551b60e7eb90ef5a3d55afed6ad212d2d8d | nrtf/exp-gan | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-10-02T10:03:57+00:00 | {"license": "cc-by-nc-sa-4.0"} | 2022-10-02T10:03:57+00:00 |
|
1f0aa574fa48c8a418312bce7db5b63e4ad8f5d6 | xueqing12/Arcane-style | [
"license:afl-3.0",
"region:us"
] | 2022-10-02T11:49:54+00:00 | {"license": "afl-3.0"} | 2022-10-02T11:49:54+00:00 |
|
8a097d8f1fe23fb1493689f89dd63637d070d640 | JannaB/dreambooth_corgie | [
"region:us"
] | 2022-10-02T12:17:20+00:00 | {} | 2022-10-02T12:45:49+00:00 |
|
cca7aee96d624adec293e9f91e3c1fded3463b55 | st4lk1981/titou | [
"license:cc",
"region:us"
] | 2022-10-02T12:27:42+00:00 | {"license": "cc"} | 2022-10-02T12:31:56+00:00 |
|
419747e72470311563b3b35b9c178dc69e3ab116 | # Dataset Card for WikiANN
## 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
- **Paper:** The original datasets come from Introducing QuBERT: A Large Monolingual Corpus and BERT Model for
Southern Quechua [paper](https://aclanthology.org/2022.deeplo-1.1.pdf) by Rodolfo Zevallos et al. (2022).
- **Point of Contact:** [Rodolfo Zevallos](mailto:[email protected])
### Dataset Summary
NER_Quechua_IIC is a named entity recognition dataset consisting of dictionary texts provided by the Peruvian Ministry of Education, annotated with LOC (location), PER (person) and ORG (organization) tags in the IOB2 format.
### Supported Tasks and Leaderboards
- `named-entity-recognition`: The dataset can be used to train a model for named entity recognition in Quechua languages.
| Llamacha/ner_quechua_iic | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"size_categories:n<1K",
"source_datasets:original",
"language:qu",
"license:apache-2.0",
"region:us"
] | 2022-10-02T13:00:17+00:00 | {"annotations_creators": ["crowdsourced"], "language": ["qu"], "license": ["apache-2.0"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"]} | 2022-10-02T13:19:29+00:00 |
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