author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1 class | downloads float64 1 1M ⌀ | gated bool 2 classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2 classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
loubnabnl | null | null | null | false | 3 | false | loubnabnl/github-clean | 2022-06-25T11:36:42.000Z | null | false | e3572e08c43f909c626027c39c7b56c8b8ee5bef | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/loubnabnl/github-clean/resolve/main/README.md | ---
license: apache-2.0
---
|
sophieb | null | null | null | false | 2 | false | sophieb/dynamically_generated_hate_speech_dataset | 2022-06-25T18:02:18.000Z | null | false | eebfc857e775f10513dd739c355e326937d58de9 | [] | [] | https://huggingface.co/datasets/sophieb/dynamically_generated_hate_speech_dataset/resolve/main/README.md | # Dataset card for dynamically generated dataset hate speech detection
## Dataset summary
This dataset that was dynamically generated for training and improving hate speech detection models. A group of trained annotators generated and labeled challenging examples so that hate speech models could be tricked and consequently improved. This dataset contains about 40,000 examples of which 54% are labeled as hate speech. It also provides the target of hate speech, including vulnerable, marginalized, and discriminated groups. Overall, this is a balanced dataset which makes it different from the already available hate speech datasets you can find on the web.
This dataset was presented in the article [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection published](https://aclanthology.org/2021.acl-long.132.pdf) in 2021. The article describes the process for generating and annotating the data. Also, it describes how they used the generated data for training and improving hate speech detection models. The full author list is the following: Bertie Vidgen (The Alan Turing Institute), Tristan Thrush (Facebook), Zeerak Waseem (University of Sheffield), and Douwe Kiela (Facebook).
|
peabits | null | null | null | false | 2 | false | peabits/a09 | 2022-06-26T03:03:46.000Z | null | false | a541059116085f520b549ab6572c65a06086d445 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/peabits/a09/resolve/main/README.md | ---
license: apache-2.0
---
|
mesolitica | null | null | null | false | 2 | false | mesolitica/crawl-cambridge-english-malaysian | 2022-10-15T09:33:19.000Z | null | false | 4f6a54fc39110a7b3289c12f58122ead7cf5dcbb | [] | [
"language:ms"
] | https://huggingface.co/datasets/mesolitica/crawl-cambridge-english-malaysian/resolve/main/README.md | ---
language: ms
---
# Crawl cambridge English-Malaysian
Crawled from https://dictionary.cambridge.org/browse/english-malaysian/, 25171 english-malaysian words.
Notebooks to gather the dataset at https://github.com/huseinzol05/malay-dataset/tree/master/dictionary/cambridge |
lewtun | null | null | null | false | 68 | false | lewtun/dog_food | 2022-07-03T05:15:18.000Z | null | false | c31bd6bbb3460267ae1da555b9804579a2f99e01 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:image-classification",
"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/lewtun/dog_food/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Dog vs Food Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for the Dog 🐶 vs. Food 🍔 (a.k.a. Dog Food) Dataset
## 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/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-
- **Repository:** : https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-
- **Paper:** : N/A
- **Leaderboard:**: N/A
- **Point of Contact:**: @sasha
### Dataset Summary
This is a dataset for multiclass image classification, between 'dog', 'chicken', and 'muffin' classes.
The 'dog' class contains images of dogs that look like fried chicken and some that look like images of muffins, while the 'chicken' and 'muffin' classes contains images of (you guessed it) fried chicken and muffins 😋
### Supported Tasks and Leaderboards
TBC
### Languages
The labels are in English (['dog', 'chicken', 'muffin'])
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x470 at 0x7F176094EF28>,
'label': 0}
}
```
### Data Fields
- img: A `PIL.JpegImageFile` object containing the 300x470. image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- label: 0-1 with the following correspondence
0 dog
1 food
### Data Splits
Train (1875 images) and Test (625 images)
## Dataset Creation
### Curation Rationale
N/A
### Source Data
#### Initial Data Collection and Normalization
This dataset was taken from the [qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins?](https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-) Github repository and randomly splitting 25% of the data for validation.
### Annotations
#### Annotation process
This data was scraped from the internet and annotated based on the query words.
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
This dataset is balanced -- it has an equal number of images of dogs (1000) compared to chicken (1000 and muffin (1000). This should be taken into account when evaluating models.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
This dataset was created by @lanceyjt, @yl3829, @wesleytao, @qw2243c and @asyouhaveknown
### Licensing Information
No information is indicated on the original [github repository](https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-).
### Citation Information
N/A
### Contributions
Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.
|
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494827 | 2022-06-26T11:26:03.000Z | null | false | 3210c118b2c5921129ee63869e0804d025a083e8 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494827/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: HrayrMSint/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: HrayrMSint/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494828 | 2022-06-26T11:27:08.000Z | null | false | 139dd68cf257ce2ea6f78625384d235ce98cb474 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494828/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: lewtun/roberta-large-finetuned-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: lewtun/roberta-large-finetuned-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494829 | 2022-06-26T11:27:14.000Z | null | false | d00db7288fa1c5448ef448afaa079ee7fc723869 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494829/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: optimum/roberta-large-finetuned-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: optimum/roberta-large-finetuned-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494830 | 2022-06-26T11:26:14.000Z | null | false | 308091a601bcff02e6b72cfab2dec043721ca47a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494830/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: MhF/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: MhF/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494831 | 2022-06-26T11:26:20.000Z | null | false | 79343efea08c58d4cb5aaa3377515681e17b8e84 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494831/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: Omar95farag/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: Omar95farag/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494832 | 2022-06-26T11:26:25.000Z | null | false | af158837c25078f3a6881133f350a87ced485365 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494832/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: abdelkader/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: abdelkader/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494835 | 2022-06-26T11:26:45.000Z | null | false | 089d3602f57afc6948cb926890662f5e190d8a1f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494835/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: jackmleitch/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: jackmleitch/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494833 | 2022-06-26T11:29:12.000Z | null | false | 523a4305c0e595c344b4d85572ba852e86042b19 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494833/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: aytugkaya/distilbert-base-uncased-finetuned-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: aytugkaya/distilbert-base-uncased-finetuned-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494836 | 2022-06-26T11:26:51.000Z | null | false | dcb235a5378155bc061bdceb73205c4308806a7a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494836/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: moshew/distilbert-base-uncased-finetuned-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: moshew/distilbert-base-uncased-finetuned-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e1907042-7494834 | 2022-06-26T11:29:24.000Z | null | false | 8570ba48a386626e7cfd4e551dfe622f8b841a34 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e1907042-7494834/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: calcworks/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: calcworks/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
ElKulako | null | null | null | false | 3 | false | ElKulako/stocktwits-crypto | 2022-09-01T00:46:26.000Z | null | false | 5b6da7cd5381ce0a79e7faa2bfd9ad18372abac7 | [] | [] | https://huggingface.co/datasets/ElKulako/stocktwits-crypto/resolve/main/README.md | Dataset StockTwits-crypto contains all cryptocurrency-related posts from the StockTwits website, from 1st of November 2021 to the 15th of June 2022.
The data has been cleaned and preprocessed, we removed:
- cashtags, hashtags, usernames,
- URLs, crypto wallets,
- Chinese, Korean and Japanese characters,
- (most) UTF-8 encoding issues
- removed all posts shorter than 4 words
- removed all duplicate posts
- fixed spacing and punctuation issues, converted all text to lowercase |
rjac | null | null | null | false | 1 | false | rjac/all-the-news-2-1-Component-one-embedding | 2022-07-18T18:09:59.000Z | null | false | b7db0a9cdf3c918e10f834240dc69f3bb68c3166 | [] | [] | https://huggingface.co/datasets/rjac/all-the-news-2-1-Component-one-embedding/resolve/main/README.md | Similar dataset to [rjac/all-the-news-2-1-Component-one](https://huggingface.co/datasets/rjac/all-the-news-2-1-Component-one) with Embedding generated by Sentence Transformer - model : "all-MiniLM-L6-v2" |
SoDehghan | null | null | null | false | 1 | false | SoDehghan/datasets_for_supmpn | 2022-06-26T18:50:11.000Z | null | false | 12e1d31710ba2c6b4a173fdd1c54504e27b81747 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/SoDehghan/datasets_for_supmpn/resolve/main/README.md | ---
license: apache-2.0
---
This repo contains datasets for our paper. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-0839fa4f-7534859 | 2022-06-26T19:42:20.000Z | null | false | 736fe56ff6f740a268dd379d455006b4abfac49d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:ag_news"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-0839fa4f-7534859/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ag_news
eval_info:
task: multi_class_classification
model: nateraw/bert-base-uncased-ag-news
metrics: []
dataset_name: ag_news
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: nateraw/bert-base-uncased-ag-news
* Dataset: ag_news
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d05a5ffd-7544860 | 2022-06-26T19:45:06.000Z | null | false | 58e47aa8905bb969ba88b7fdd3afdb60bce83959 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:trec"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d05a5ffd-7544860/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- trec
eval_info:
task: multi_class_classification
model: aychang/bert-base-cased-trec-coarse
metrics: []
dataset_name: trec
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label-coarse
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: aychang/bert-base-cased-trec-coarse
* Dataset: trec
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d05a5ffd-7544861 | 2022-06-26T19:43:02.000Z | null | false | 7b4c75012546212bf38f998f18fb697107201a2e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:trec"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d05a5ffd-7544861/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- trec
eval_info:
task: multi_class_classification
model: aychang/distilbert-base-cased-trec-coarse
metrics: []
dataset_name: trec
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label-coarse
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: aychang/distilbert-base-cased-trec-coarse
* Dataset: trec
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-3aabac9e-7554863 | 2022-06-26T19:58:52.000Z | null | false | 5d1738163151b8e352a623484b42035d01da5fae | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-3aabac9e-7554863/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: ahmeddbahaa/xlmroberta-finetune-en-cnn
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: ahmeddbahaa/xlmroberta-finetune-en-cnn
* Dataset: cnn_dailymail
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-3aabac9e-7554868 | 2022-06-26T20:58:35.000Z | null | false | 08ef47c03de533bc48e17d7e2b5b0517976a5e9b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-3aabac9e-7554868/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: eslamxm/mbart-finetune-en-cnn
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: eslamxm/mbart-finetune-en-cnn
* Dataset: cnn_dailymail
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-3aabac9e-7554869 | 2022-06-26T19:57:41.000Z | null | false | ea4825976f3f6de4b1c52d0764e2f8efb3f79a55 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-3aabac9e-7554869/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: flax-community/t5-base-cnn-dm
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: flax-community/t5-base-cnn-dm
* Dataset: cnn_dailymail
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-2f2d3a43-7564875 | 2022-06-26T19:56:39.000Z | null | false | 5c91d83eea059c858167c9d7aba66ce1f6bd72f6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:wikiann"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-2f2d3a43-7564875/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- wikiann
eval_info:
task: entity_extraction
model: Ravindra001/bert-finetuned-ner
metrics: []
dataset_name: wikiann
dataset_config: en
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Ravindra001/bert-finetuned-ner
* Dataset: wikiann
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
rjac | null | null | null | false | 3 | false | rjac/all-the-news-2-1-Component-one-sentence-embedding | 2022-06-27T12:31:21.000Z | null | false | 73083debe3671f42d430d9c0ad660a4fca88796c | [] | [] | https://huggingface.co/datasets/rjac/all-the-news-2-1-Component-one-sentence-embedding/resolve/main/README.md | Similar dataset to [rjac/all-the-news-2-1-Component-one](https://huggingface.co/datasets/rjac/all-the-news-2-1-Component-one) with Embedding generated by Sentence Transformer - model : "all-MiniLM-L6-v2" per small paragraph of an Article.
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574879 | 2022-06-26T20:12:39.000Z | null | false | 73c75a3f2fc58422fcf0d555b397bd578dcf4992 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574879/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Akshat/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Akshat/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574881 | 2022-06-26T20:10:59.000Z | null | false | 26adeb3081069e4817f63dcb62b1eb9140baafaa | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574881/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Cole/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Cole/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574882 | 2022-06-26T20:11:28.000Z | null | false | 727df2a9c9c11c25d79f06ceaea6c8724fee5bfc | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574882/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Gerard/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Gerard/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574880 | 2022-06-26T20:13:47.000Z | null | false | a4bba63772c141cb9d4a8f9a7b78063afac563a3 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574880/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Andyrasika/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Andyrasika/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574883 | 2022-06-26T20:11:34.000Z | null | false | d888fc3c84f9de412ac8d81cc2d35aba646d1843 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574883/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: KayKozaronek/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: KayKozaronek/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574884 | 2022-06-26T20:11:24.000Z | null | false | 3d278a75960eb211a6e7a141630d9d1425bf602b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574884/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Leizhang/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Leizhang/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574885 | 2022-06-26T20:11:28.000Z | null | false | aeaef0d1cf99758936dfbd65179f38ef512a8053 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574885/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: MhF/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: MhF/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574886 | 2022-06-26T20:11:34.000Z | null | false | 9c5d7d1cc44e74d0141da4452133f7dfabf08209 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574886/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Ning-fish/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Ning-fish/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574887 | 2022-06-26T20:11:40.000Z | null | false | b7b163b11963acee53bdfe80ff06235c76b14119 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574887/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Ninh/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Ninh/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574888 | 2022-06-26T20:11:48.000Z | null | false | df3f776ab930859fdbaa0be25c9dac5dd02611f4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574888/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: OneFly/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: OneFly/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-bc0462a6-7584891 | 2022-06-26T20:12:49.000Z | null | false | 9d50a177394677964a26dd2ecfbbc1530830d0c6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-bc0462a6-7584891/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Shenghao1993/xlm-roberta-base-finetuned-panx-fr
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.fr
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Shenghao1993/xlm-roberta-base-finetuned-panx-fr
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-bc0462a6-7584893 | 2022-06-26T20:13:17.000Z | null | false | 67111d7eec91e1444ae992f6634227a5e84c8f47 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-bc0462a6-7584893/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: olpa/xml-roberta-base-finetuned-panx-fr
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.fr
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: olpa/xml-roberta-base-finetuned-panx-fr
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-bc0462a6-7584895 | 2022-06-26T20:13:18.000Z | null | false | 6c2d6b0fa7b1a83828c1c24bb4a3d0bf0a5118e9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-bc0462a6-7584895/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: moghis/xlm-roberta-base-finetuned-panx-fr
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.fr
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: moghis/xlm-roberta-base-finetuned-panx-fr
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-0a15404e-7594901 | 2022-06-26T20:15:10.000Z | null | false | 21052961d6b846097b36bafb01a63a832a6e5c91 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-0a15404e-7594901/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: moghis/xlm-roberta-base-finetuned-panx-it
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.it
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: moghis/xlm-roberta-base-finetuned-panx-it
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d578e0ca-7604911 | 2022-06-26T20:16:39.000Z | null | false | 4f3c3daa391f4a5fabea115a410648e57832bb7a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xtreme"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d578e0ca-7604911/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: moghis/xlm-roberta-base-finetuned-panx-en
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.en
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: moghis/xlm-roberta-base-finetuned-panx-en
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-019e0f0d-7644945 | 2022-06-26T23:46:29.000Z | null | false | b2a38440e30ffb12f0b4279d49293bf77b59311f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:scientific_papers"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-019e0f0d-7644945/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- scientific_papers
eval_info:
task: summarization
model: google/bigbird-pegasus-large-pubmed
metrics: []
dataset_name: scientific_papers
dataset_config: pubmed
dataset_split: test
col_mapping:
text: article
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-pubmed
* Dataset: scientific_papers
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d47ba8c2-7654948 | 2022-06-26T23:44:04.000Z | null | false | ae50b1ecd96a469dd52c3d1b37c31dd2a996491e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:scientific_papers"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d47ba8c2-7654948/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- scientific_papers
eval_info:
task: summarization
model: google/bigbird-pegasus-large-arxiv
metrics: []
dataset_name: scientific_papers
dataset_config: arxiv
dataset_split: test
col_mapping:
text: article
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-arxiv
* Dataset: scientific_papers
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d47ba8c2-7654949 | 2022-06-26T23:45:21.000Z | null | false | 3b1f469369c1cfdf469976e8b5f361f914bfe965 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:scientific_papers"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d47ba8c2-7654949/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- scientific_papers
eval_info:
task: summarization
model: google/bigbird-pegasus-large-pubmed
metrics: []
dataset_name: scientific_papers
dataset_config: arxiv
dataset_split: test
col_mapping:
text: article
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-pubmed
* Dataset: scientific_papers
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-0b0f26eb-7664950 | 2022-06-26T20:32:34.000Z | null | false | fc98e2a9feddabebccaf376bea6e5f94473c8537 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:lener_br"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-0b0f26eb-7664950/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- lener_br
eval_info:
task: entity_extraction
model: Luciano/bertimbau-base-lener_br
metrics: []
dataset_name: lener_br
dataset_config: lener_br
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Luciano/bertimbau-base-lener_br
* Dataset: lener_br
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-0b0f26eb-7664951 | 2022-06-26T20:35:49.000Z | null | false | c04d5f52a828fa57fc798a4a4b79ee9e82d52940 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:lener_br"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-0b0f26eb-7664951/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- lener_br
eval_info:
task: entity_extraction
model: Luciano/bertimbau-large-lener_br
metrics: []
dataset_name: lener_br
dataset_config: lener_br
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Luciano/bertimbau-large-lener_br
* Dataset: lener_br
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-6971abf9-7684954 | 2022-06-26T20:37:24.000Z | null | false | 1de7c600000a6d8186cec9965df97910ae72c28c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-6971abf9-7684954/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-ner-amharic
metrics: []
dataset_name: masakhaner
dataset_config: amh
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-ner-amharic
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-6971abf9-7684956 | 2022-06-26T20:37:31.000Z | null | false | 862bba82d3069ff7c2652f7de36248e067159363 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-6971abf9-7684956/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili
metrics: []
dataset_name: masakhaner
dataset_config: amh
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-6971abf9-7684957 | 2022-06-26T20:37:33.000Z | null | false | 0465942446b2c98a9f5efac91edc4380aa3247b4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-6971abf9-7684957/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic
metrics: []
dataset_name: masakhaner
dataset_config: amh
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-6971abf9-7684955 | 2022-06-26T20:39:59.000Z | null | false | 702c383a751576b6d21051dff9ea8af71a5f0e9b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-6971abf9-7684955/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic
metrics: []
dataset_name: masakhaner
dataset_config: amh
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-200453bd-7694959 | 2022-06-26T20:38:03.000Z | null | false | fc5a4b24d3683be8d9c183c07baf1bb65b1b6980 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-200453bd-7694959/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: arnolfokam/bert-base-uncased-swa
metrics: []
dataset_name: masakhaner
dataset_config: swa
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: arnolfokam/bert-base-uncased-swa
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-200453bd-7694960 | 2022-06-26T20:38:14.000Z | null | false | 52bfa7c79752f42400bae49ff02eb0e159934e67 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-200453bd-7694960/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: arnolfokam/mbert-base-uncased-swa
metrics: []
dataset_name: masakhaner
dataset_config: swa
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: arnolfokam/mbert-base-uncased-swa
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-200453bd-7694961 | 2022-06-26T20:38:19.000Z | null | false | e75123b7e46a48a69278563d09d968f51eebde56 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-200453bd-7694961/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: arnolfokam/mbert-base-uncased-ner-swa
metrics: []
dataset_name: masakhaner
dataset_config: swa
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: arnolfokam/mbert-base-uncased-ner-swa
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-200453bd-7694962 | 2022-06-26T20:38:35.000Z | null | false | fc0435887fc54bc703347b13c25ac39fb3413217 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-200453bd-7694962/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-ner-swahili
metrics: []
dataset_name: masakhaner
dataset_config: swa
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-ner-swahili
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-200453bd-7694963 | 2022-06-26T20:38:38.000Z | null | false | 43d7585b40d2dcb9ffa5c80e2d42ac2f634c2875 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-200453bd-7694963/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-luo-finetuned-ner-swahili
metrics: []
dataset_name: masakhaner
dataset_config: swa
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-luo-finetuned-ner-swahili
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-200453bd-7694964 | 2022-06-26T20:39:11.000Z | null | false | ba02b413d1217559c3274f72751aa3efd0956470 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-200453bd-7694964/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili
metrics: []
dataset_name: masakhaner
dataset_config: swa
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-200453bd-7694965 | 2022-06-26T20:38:53.000Z | null | false | 430862f84254922abed2ff52371c235f27b92fe8 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-200453bd-7694965/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof
metrics: []
dataset_name: masakhaner
dataset_config: swa
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-200453bd-7694966 | 2022-06-26T20:38:59.000Z | null | false | bd322d79fe53c69df0e3d268681abc99e03ffbda | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-200453bd-7694966/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-luo
metrics: []
dataset_name: masakhaner
dataset_config: swa
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-luo
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-ab647f27-7704968 | 2022-06-26T20:39:12.000Z | null | false | 2bed5936e172bfe81d38c91685f0ecb62bed5a2a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-ab647f27-7704968/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-ner-yoruba
metrics: []
dataset_name: masakhaner
dataset_config: yor
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-ner-yoruba
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-ab647f27-7704969 | 2022-06-26T20:39:18.000Z | null | false | 4132c26b1e8d26dd8f566b19b7282004302c38ae | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-ab647f27-7704969/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba
metrics: []
dataset_name: masakhaner
dataset_config: yor
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-ab647f27-7704970 | 2022-06-26T20:39:27.000Z | null | false | 090405d02d57968300ce05415d1df3cce1db7cee | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-ab647f27-7704970/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili
metrics: []
dataset_name: masakhaner
dataset_config: yor
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-ab647f27-7704971 | 2022-06-26T20:39:32.000Z | null | false | 3040aff916ba4284b4324e9e19756c309739387f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:masakhaner"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-ab647f27-7704971/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- masakhaner
eval_info:
task: entity_extraction
model: mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-yoruba
metrics: []
dataset_name: masakhaner
dataset_config: yor
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-yoruba
* Dataset: masakhaner
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
charlesmichaelvaughn | null | null | null | false | 1 | false | charlesmichaelvaughn/charlesmichaelvaughn | 2022-06-27T04:53:23.000Z | null | false | 2f3638e6fe9986450163567e06c757188bc54991 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/charlesmichaelvaughn/charlesmichaelvaughn/resolve/main/README.md | ---
license: apache-2.0
---
|
lamarvandusen | null | null | null | false | 1 | false | lamarvandusen/lamarvandusen | 2022-06-27T06:14:36.000Z | null | false | a1b5eceac63a5f3e471a798b79397d3c33b4a962 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/lamarvandusen/lamarvandusen/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-8ef742e5-7734972 | 2022-06-27T07:48:40.000Z | null | false | 9e2d4bbce448f22bb4428131a02a400357ef4301 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-8ef742e5-7734972/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: mrp/bert-finetuned-squad
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mrp/bert-finetuned-squad
* Dataset: squad
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@thomwolf](https://huggingface.co/thomwolf) for evaluating this model. |
Datatang | null | null | null | false | 1 | false | Datatang/Human_Face_Image_Data_with_Multiple_Angles_Light_Conditions_and_Expressions | 2022-06-28T03:36:45.000Z | null | false | 2e4f6ee1178f9561e2ecfc5de3893f744fe8b145 | [] | [] | https://huggingface.co/datasets/Datatang/Human_Face_Image_Data_with_Multiple_Angles_Light_Conditions_and_Expressions/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Human_Face_Image_Data_with_Multiple_Angles_Light_Conditions_and_Expressions
## 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://bit.ly/3HUjRLy
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
110 People – Human Face Image Data with Multiple Angles, Light Conditions, and Expressions. The subjects are all young people. For each subject, 2,100 images were collected. The 2,100 images includes 14 kinds of camera angles *5 kinds of light conditions * 30 kinds of expressions. The data can be used for face recognition, 3D face reconstruction, etc.
For more details, please refer to the link: https://bit.ly/3HUjRLy
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Multi-pose_and_Multi-expression_Face_Data | 2022-06-28T03:15:56.000Z | null | false | b07b54e5394c2a04756cab09e61dc0a3ba115eb4 | [] | [] | https://huggingface.co/datasets/Datatang/Multi-pose_and_Multi-expression_Face_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Multi-pose_and_Multi-expression_Face_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3xWSKej
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
1,507 People 102,476 Images Multi-pose and Multi-expression Face Data. The data includes 1,507 Chinese people (762 males, 745 females). For each subject, 62 multi-pose face images and 6 multi-expression face images were collected. The data diversity includes multiple angles, multiple poses and multple light conditions image data from all ages. This data can be used for tasks such as face recognition and facial expression recognition.
For more details, please refer to the link: https://bit.ly/3xWSKej
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Driver_Behavior_Collection_Data | 2022-06-28T03:05:10.000Z | null | false | 3cae9920f70448194ab6105acad2906b741aeefb | [] | [] | https://huggingface.co/datasets/Datatang/Driver_Behavior_Collection_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Driver_Behavior_Collection_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/39VohoQ
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
1,003 People-Driver Behavior Collection Data. The data includes multiple ages and multiple time periods. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis.
For more details, please refer to the link: https://bit.ly/39VohoQ
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
Datatang | null | null | null | false | 7 | false | Datatang/Infrared_Face_Recognition_Data | 2022-06-28T03:36:35.000Z | null | false | a2e2216502bcb2bc188292209f4daff27b4183a9 | [] | [] | https://huggingface.co/datasets/Datatang/Infrared_Face_Recognition_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Infrared_Face_Recognition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3HUjKj6
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
4,134 People – Infrared Face Recognition Data. The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is realsense D453i. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition.
For more details, please refer to the link: https://bit.ly/3HUjKj6
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
Datatang | null | null | null | false | 1 | false | Datatang/Passenger_Behavior_Recognition_Data | 2022-06-28T03:15:24.000Z | null | false | 151251d7bd08c03d08d55d1aac726d5fb571c75a | [] | [] | https://huggingface.co/datasets/Datatang/Passenger_Behavior_Recognition_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Passenger_Behavior_Recognition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3Ot19xg
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
122 People - Passenger Behavior Recognition Data. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). The passenger behaviors include passenger normal behavior, passenger abnormal behavior(passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as passenger behavior analysis.
For more details, please refer to the link: https://bit.ly/3Ot19xg
### Supported Tasks and Leaderboards
face-detection, computer-vision, object-detection: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Multi-race_Driver_Behavior_Collection_Data | 2022-06-28T03:15:02.000Z | null | false | 63274cd2726f459f235e70f0f86880e1ee8d579a | [] | [] | https://huggingface.co/datasets/Datatang/Multi-race_Driver_Behavior_Collection_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Multi-race_Driver_Behavior_Collection_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3xXaLZV
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
304 People Multi-race - Driver Behavior Collection Data. The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis.
For more details, please refer to the link: https://bit.ly/3xXaLZV
### Supported Tasks and Leaderboards
face-detection, computer-vision, object-detection: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Face_Recognition_Data_with_Gauze_Mask | 2022-06-28T03:36:35.000Z | null | false | 399aa62715ecaf461d3ca97bc2620df3d000d6d2 | [] | [] | https://huggingface.co/datasets/Datatang/Face_Recognition_Data_with_Gauze_Mask/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Face_Recognition_Data_with_Gauze_Mask
## 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://bit.ly/3a0NLRL
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
5,030 People - Face Recognition Data with Gauze Mask, for each subject, 7 images were collected. The dataset diversity includes multiple mask types, multiple ages, multiple light conditions and scenes.This data can be applied to computer vision tasks such as occluded face detection and recognition.
For more details, please refer to the link: https://bit.ly/3a0NLRL
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Occluded_and_Multi-pose_Face_Recognition_Data | 2022-06-28T03:13:53.000Z | null | false | 181effebe1094fa3ee23f9dbb5d255f4c2fe92a7 | [] | [] | https://huggingface.co/datasets/Datatang/Occluded_and_Multi-pose_Face_Recognition_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/MOccluded_and_Multi-pose_Face_Recognition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3HSB1co
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
1,930 People with Occlusion and Multi-pose Face Recognition Data, for each subject, 200 images were collected. The 200 images includes 4 kinds of light conditions * 10 kinds of occlusion cases (including non-occluded case) * 5 kinds of face pose. This data can be applied to computer vision tasks such as occluded face detection and recognition.
For more details, please refer to the link: https://bit.ly/3HSB1co
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 2 | false | Datatang/Handwriting_OCR_Data_of_Japanese_and_Korean | 2022-06-28T03:16:54.000Z | null | false | 5d5b906c6b9e80ef62741d2b3567b6b5e844fa23 | [] | [] | https://huggingface.co/datasets/Datatang/Handwriting_OCR_Data_of_Japanese_and_Korean/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Handwriting_OCR_Data_of_Japanese_and_Korean
## 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://bit.ly/3HWadrP
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
100 People - Handwriting OCR Data of Japanese and Korean,. This dadaset was collected from 100 subjects including 50 Japanese, 49 Koreans and 1 Afghan. For different subjects, the corpus are different. The data diversity includes multiple cellphone models and different corpus. This dataset can be used for tasks, such as handwriting OCR data of Japanese and Korean.
For more details, please refer to the link: https://bit.ly/3HWadrP
### Supported Tasks and Leaderboards
image-to-text, computer-vision: The dataset can be used to train a model for image-to-text.
### Languages
Japanese, Korean
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
Datatang | null | null | null | false | 10 | false | Datatang/Natural_Scenes_OCR_Data_of_12_Languages | 2022-06-28T03:17:46.000Z | null | false | d355802f88f946262a52ee3ddad388dc791e5c09 | [] | [] | https://huggingface.co/datasets/Datatang/Natural_Scenes_OCR_Data_of_12_Languages/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Natural_Scenes_OCR_Data_of_12_Languages
## 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://bit.ly/3y30wn8
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
105,941 Images Natural Scenes OCR Data of 12 Languages. The data covers 12 languages (6 Asian languages, 6 European languages), multiple natural scenes, multiple photographic angles. For annotation, line-level quadrilateral bounding box annotation and transcription for the texts were annotated in the data. The data can be used for tasks such as OCR of multi-language.
For more details, please refer to the link: https://bit.ly/3y30wn8
### Supported Tasks and Leaderboards
image-to-text, computer-vision: The dataset can be used to train a model for image-to-text.
### Languages
Japanese, Korean, Indonesian, Malay, Vietnamese, Thai, French, German, Italian, Portuguese, Russian and Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
Datatang | null | null | null | false | 1 | false | Datatang/Living_Face_Anti-Spoofing_Data | 2022-06-28T03:18:15.000Z | null | false | bf9196e947a678ff2b0e1cedcbd20f2f731f0f3c | [] | [] | https://huggingface.co/datasets/Datatang/Living_Face_Anti-Spoofing_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Living_Face_Anti-Spoofing_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3byhJx6
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
1,056 People Living_face & Anti-Spoofing Data. The collection scenes include indoor and outdoor scenes. The data includes male and female. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The data includes multiple postures, multiple expressions, and multiple anti-spoofing samples. The data can be used for tasks such as face payment, remote ID authentication, and face unlocking of mobile phone.
For more details, please refer to the link: https://bit.ly/3byhJx6
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
Datatang | null | null | null | false | 1 | false | Datatang/3D_Living_Face_Anti_Spoofing_Data | 2022-06-28T03:36:19.000Z | null | false | 493b7f3f63f7aa27a70bb552a61f5cb7c1b8d7d1 | [] | [] | https://huggingface.co/datasets/Datatang/3D_Living_Face_Anti_Spoofing_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/3D_Living_Face_Anti_Spoofing_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3QL85Y9
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
1,417 People – 3D Living_Face & Anti_Spoofing Data. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing.
For more details, please refer to the link: https://bit.ly/3QL85Y9
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Multi-race_7_Expressions_Recognition_Data | 2022-06-28T03:11:52.000Z | null | false | 73c26dcc0cb2303acf1c6dd15edb56138e5c70d9 | [] | [] | https://huggingface.co/datasets/Datatang/Multi-race_7_Expressions_Recognition_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Multi-race_7_Expressions_Recognition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3HS20oG
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
25,998 People Multi-race 7 Expressions Recognition Data. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. For each person, 7 images were collected. The data diversity includes different facial postures, different expressions, different light conditions and different scenes. The data can be used for tasks such as face expression recognition.
For more details, please refer to the link: https://bit.ly/3HS20oG
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
Datatang | null | null | null | false | 1 | false | Datatang/50_Types_of_Dynamic_Gesture_Recognition_Data | 2022-06-28T03:10:55.000Z | null | false | bf36ae86e873991ac1c298400acf5d4299d6edaf | [] | [] | https://huggingface.co/datasets/Datatang/50_Types_of_Dynamic_Gesture_Recognition_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/50_Types_of_Dynamic_Gesture_Recognition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3bu6Wns
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
558,870 Videos - 50 Types of Dynamic Gesture Recognition Data. The collecting scenes of this dataset include indoor scenes and outdoor scenes (natural scenery, street view, square, etc.). The data covers males and females (Chinese). The age distribution ranges from teenager to senior. The data diversity includes multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances. This data can be used for dynamic gesture recognition of smart homes, audio equipments and on-board systems.
For more details, please refer to the link: https://bit.ly/3bu6Wns
### Supported Tasks and Leaderboards
object-detection, computer-vision: The dataset can be used to train a model for object detection.
### Languages
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Multi-race_and_Multi-pose_Face_Images_Data | 2022-06-28T03:12:36.000Z | null | false | 6ea5b7a6f19602f17d506d94493e13de83a4e78c | [] | [] | https://huggingface.co/datasets/Datatang/Multi-race_and_Multi-pose_Face_Images_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Multi-race_and_Multi-pose_Face_Images_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3HYevPp
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
23,110 People Multi-race and Multi-pose Face Images Data. This data includes Asian race, Caucasian race, black race, brown race and Indians. Each subject were collected 29 images under different scenes and light conditions. The 29 images include 28 photos (multi light conditions, multiple poses and multiple scenes) + 1 ID photo. This data can be used for face recognition related tasks.
For more details, please refer to the link: https://bit.ly/3HYevPp
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 20 | false | Datatang/3D_Instance_Segmentation_and_22_Landmarks_Annotation_Data_of_Human_Body | 2022-06-28T03:37:23.000Z | null | false | 99748211c77188e508cfe9eac3e47d0bb9880a40 | [] | [] | https://huggingface.co/datasets/Datatang/3D_Instance_Segmentation_and_22_Landmarks_Annotation_Data_of_Human_Body/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/3D_Instance_Segmentation_and_22_Landmarks_Annotation_Data_of_Human_Body
## 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://bit.ly/3bziia1
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
18,880 Images of 466 People - 3D Instance Segmentation and 22 Landmarks Annotation Data of Human Body. The dataset diversity includes multiple scenes, light conditions, ages, shooting angles, and poses. In terms of annotation, we adpoted instance segmentation annotations on human body. 22 landmarks were also annotated for each human body. The dataset can be used for tasks such as human body instance segmentation and human behavior recognition.
For more details, please refer to the link: https://bit.ly/3bziia1
### Supported Tasks and Leaderboards
instance-segmentation, computer-vision,image-segmentation: The dataset can be used to train a model for computer vision.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 33 | false | Datatang/Human_Facial_Skin_Defects_Data | 2022-06-28T03:08:35.000Z | null | false | 0d5e83cef577a5b17c132159ec46313d8c7cbaaa | [] | [] | https://huggingface.co/datasets/Datatang/Human_Facial_Skin_Defects_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Human_Facial_Skin_Defects_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3R2Pmrd
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
4,788 Chinese people 5,105 images Human Facial Skin Defects Data. The data includes the following five types of facial skin defects: acne, acne marks, stains, wrinkles and dark circles. This data can be used for tasks such as skin defects detection.
For more details, please refer to the link: https://bit.ly/3R2Pmrd
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 5 | false | Datatang/Multi-class_Fashion_Item_Detection_Data | 2022-06-28T03:35:53.000Z | null | false | bcf0269241c4133ccc369a78a4ed79ce9d37ee0c | [] | [] | https://huggingface.co/datasets/Datatang/Multi-class_Fashion_Item_Detection_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Multi-class_Fashion_Item_Detection_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3y0mLd0
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
144,810 Images Multi-class Fashion Item Detection Data. In this dataset, 19,968 images of male and 124,842 images of female were included. The Fashion Items were divided into 4 parts based on the season (spring, autumn, summer and winter). In terms of annotation, rectangular bounding boxes were adopted to annotate fashion items. The data can be used for tasks such as fashion items detection, fashion recommendation and other tasks.
For more details, please refer to the link: https://bit.ly/3y0mLd0
### Supported Tasks and Leaderboards
object-detection, computer-vision: The dataset can be used to train a model for object detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/3D_Face_Recognition_Images_Data | 2022-06-28T03:35:15.000Z | null | false | 45462253bbdfe6118ef4b7118b6fd52239f9d765 | [] | [] | https://huggingface.co/datasets/Datatang/3D_Face_Recognition_Images_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/3D_Face_Recognition_Images_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3xRmXeK
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
5,199 People – 3D Face Recognition Images Data. The collection scene is indoor scene. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes multiple facial postures, multiple light conditions, multiple indoor scenes. This data can be used for tasks such as 3D face recognition.
For more details, please refer to the link: https://bit.ly/3xRmXeK
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
Datatang | null | null | null | false | 3 | false | Datatang/3D_Facial_Expressions_Recognition_Data | 2022-06-28T03:33:12.000Z | null | false | fafd9941de23757bb90a0d63abc4537adaf16e00 | [] | [] | https://huggingface.co/datasets/Datatang/3D_Facial_Expressions_Recognition_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/3D_Facial_Expressions_Recognition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3xZlC5A
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition.
For more details, please refer to the link: https://bit.ly/3xZlC5A
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
pfin123 | null | null | null | false | 1 | false | pfin123/hindi-aggregated | 2022-07-05T10:06:36.000Z | null | false | f717035e2e770ba73862314f470b82c64de89bd9 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/pfin123/hindi-aggregated/resolve/main/README.md | ---
license: apache-2.0
---
|
Datatang | null | null | null | false | 1 | false | Datatang/3D_Face_Anti_Spoofing_Data | 2022-06-28T03:31:18.000Z | null | false | 52a726059264cfeb7d18cf49a6033f551b81e43f | [] | [] | https://huggingface.co/datasets/Datatang/3D_Face_Anti_Spoofing_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/3D_Face_Anti_Spoofing_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/3xZlPpo
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
40 People - 3D Living_Face & Anti_Spoofing Data. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing.
For more details, please refer to the link: https://bit.ly/3xZlPpo
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Human_Pose_Recognition_Data | 2022-06-28T03:31:44.000Z | null | false | d47da8c5605d013944abbfcb778effc35fc0da20 | [] | [] | https://huggingface.co/datasets/Datatang/Human_Pose_Recognition_Data/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Human_Pose_Recognition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bit.ly/39YrcNx
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
10,000 People - Human Pose Recognition Data. This dataset includes indoor and outdoor scenes.This dataset covers males and females. Age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The data diversity includes different shooting heights, different ages, different light conditions, different collecting environment, clothes in different seasons, multiple human poses. For each subject, the labels of gender, race, age, collecting environment and clothes were annotated. The data can be used for human pose recognition and other tasks.
For more details, please refer to the link: https://bit.ly/39YrcNx
### Supported Tasks and Leaderboards
object-detection, computer-vision: The dataset can be used to train a model for object detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Re-ID_Data_in_Surveillance_Scenes | 2022-06-28T03:31:02.000Z | null | false | dbcea4fe8ec80611b55ca4506277b0905104d9a5 | [] | [] | https://huggingface.co/datasets/Datatang/Re-ID_Data_in_Surveillance_Scenes/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Re-ID_Data_in_Surveillance_Scenes
## 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://bit.ly/3OqJXrU
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
10,000 People - Re-ID Data in Surveillance Scenes. The data includes indoor scenes and outdoor scenes. The data includes males and females, and the age distribution is from children to the elderly. The data diversity includes different age groups, different time periods, different shooting angles, different human body orientations and postures, clothing for different seasons. For annotation, the rectangular bounding boxes and 15 attributes of human body were annotated. The data can be used for re-id and other tasks.
For more details, please refer to the link: https://bit.ly/3OqJXrU
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 1 | false | Datatang/Re-ID_Data_in_Real_Surveillance_Scenes | 2022-06-28T03:31:01.000Z | null | false | 6f0836336ea7823834cfe9f32b323633e6e34e1c | [] | [] | https://huggingface.co/datasets/Datatang/Re-ID_Data_in_Real_Surveillance_Scenes/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Re-ID_Data_in_Real_Surveillance_Scenes
## 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://bit.ly/3a20S58
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
10,000 People - Re-ID Data in Real Surveillance Scenes. The data includes indoor scenes and outdoor scenes. The data includes males and females, and the age distribution is from children to the elderly. The data diversity includes different age groups, different time periods, different shooting angles, different human body orientations and postures, clothing for different seasons. For annotation, the rectangular bounding boxes and 15 attributes of human body were annotated. This data can be used for re-id and other tasks.
For more details, please refer to the link: https://bit.ly/3a20S58
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
projecte-aina | null | \ | The CA-ZH Parallel Corpus is a Catalan-Chinese dataset of mutual translations automatically crawled from Wikipedia. Two separate corpora are included, namely CA-ZH 1.05 Wikipedia and CA-ZH 1.10 Wikipedia, the latter has better general quality than the former. The dataset was created to support Catalan NLP tasks, e.g. Machine Translation. | false | 1 | false | projecte-aina/ca_zh_wikipedia | 2022-10-26T15:10:01.000Z | null | false | f49741cc66441247f73324b27d7867eb50ab17e8 | [] | [
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"language:ca",
"language:zh",
"license:cc-by-4.0",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/projecte-aina/ca_zh_wikipedia/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- ca
- zh
license:
- cc-by-4.0
multilinguality:
- translation
pretty_name: CA-ZH Wikipedia Parallel Corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-ZH Wikipedia datasets
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [cescolano3@gmail.com](cescolano3@gmail.com)
### Dataset Summary
The CA-ZH Parallel Corpus is a Catalan-Chinese dataset of mutual translations automatically crawled from Wikipedia. Two separate corpora are included, namely CA-ZH 1.05 Wikipedia and CA-ZH 1.10 Wikipedia, the latter has better general quality than the former. The dataset was created to support Catalan NLP tasks, e.g., Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score. The dataset can be used to finetune a large-scale multilingual MT system such as m2m-100.
### Languages
The texts in the dataset are in Catalan and Chinese.
## Dataset Structure
### Data Instances
A typical data point comprises a pair of translations in Catalan and Chinese. An example from the Ca-Zh Parallel Corpus looks as follows:
```
{ "ca": "1591è Batalló Separat d'Artilleria autorpopulsada", "zh": "第1591自走砲营" }
```
### Data Fields
- "ca": Text in Catalan.
- "zh": Text in Chinese.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Curation Rationale
The Ca-Zh Parallel Corpus was built to provide more language data for MT tasks dedicated to low-resource languages. The dataset was built by gathering texts on the same topic in Catalan and Chinese from Wikipedia.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by automatic crawling, a quality filter was applied to improve the data quality. The original Chinese data was mixed into Traditional Chinese and Simplified Chinese, a simplification process was conducted in order to guarantee the unification.
#### Who are the source language producers?
All the texts in this dataset come from the Wikipedia.
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machines Translation tasks for low-resource languages such as Catalan.
### Discussion of Biases
We are aware that since the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
Wikipedia provides data of a more general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Dataset Curators
Carlos Escolano, Chenuye Zhou and Zixuan Liu, Barcelona Supercomputing Center (cescolano3 at gmail dot com)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution Share Alike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@mastersthesis{MasterThesisChenuyeZhou,
author = "Chenuye Zhou",
title = "Building a Catalan-Chinese parallel corpus for use in MT",
school = "Universitat Pompeu Fabra",
year = 2022,
address = "Barcelona",
month = jul
}
@mastersthesis{MasterThesisZixuanLiu,
author = "Zixuan Liu",
title = "Improving Chinese-Catalan Machine Translation with Wikipedia Parallel",
school = "Universitat Pompeu Fabra",
year = 2022,
address = "Barcelona",
month = jul
}
```
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-6489fc46-7764973 | 2022-06-27T09:23:03.000Z | null | false | 2ccf6f2cb7b6d504ef59891456deb65f3431c8a3 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-6489fc46-7764973/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: binary_classification
model: distilbert-base-uncased-finetuned-sst-2-english
metrics: []
dataset_name: glue
dataset_config: sst2
dataset_split: validation
col_mapping:
text: sentence
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: distilbert-base-uncased-finetuned-sst-2-english
* Dataset: glue
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-6489fc46-7764981 | 2022-06-27T09:23:45.000Z | null | false | 36852a8d4551d9fe26d0971a915480590d2f2a21 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-6489fc46-7764981/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: binary_classification
model: winegarj/distilbert-base-uncased-finetuned-sst2
metrics: []
dataset_name: glue
dataset_config: sst2
dataset_split: validation
col_mapping:
text: sentence
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: winegarj/distilbert-base-uncased-finetuned-sst2
* Dataset: glue
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
projecte-aina | null | null | null | false | 1 | false | projecte-aina/cat_manynames | 2022-07-14T10:29:54.000Z | null | false | 98964fbfb9944348a473bd4c129e0f050e68d600 | [] | [
"annotations_creators:machine-generated",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"language:ca",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:imag... | https://huggingface.co/datasets/projecte-aina/cat_manynames/resolve/main/README.md | ---
annotations_creators:
- machine-generated
- crowdsourced
language_creators:
- machine-generated
- crowdsourced
language:
- ca
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CAT ManyNames
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
---
# Dataset Card for CAT ManyNames
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/MarDominguezOrfila/CAT-ManyNames
- **Point of Contact:** [mar.dominguez01@estudiant.upf.edu](mar.dominguez01@estudiant.upf.edu)
### Dataset Summary
CAT ManyNames is the Catalan version of the [ManyNames dataset](https://github.com/amore-upf/manynames) suitable for training Language & Vision models in the task of object naming. The corpus consists of more than 23K images and their corresponding annotations.
The human-annotated test set has been built to evaluate the quality of the CAT ManyNames dataset. Its corpus consists of 1,072 images and their corresponding annotations (ca. 10 annotations per image).
### Supported Tasks and Leaderboards
Object Naming, Language & Vision Model.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
### Data Instances
[](https://object-naming-amore.upf.edu//691_3788097_singleton_obj.png "image")
`'responses' : {"guepard": 27, "lleopard": 3, "animal": 2, "gat": 2, "tigre": 2}`
### Data Fields
Both CAT ManyNames and its human-annotated test set are provided in a tab-separated text file (.tsv). The first rows contain the column labels. Nested data is stored as Python dictionaries (i.e., "{key: value}").
The columns are labelled as follows (the most important columns are listed first):
- responses: Correct responses and their counts
- topname: The most frequent name of the object in the largest cluster
- domain: The MN domain of the object
- incorrect (not available for the human-annotated test set): Incorrect responses and their counts
- singletons (not available for the human-annotated test set): All responses which were given only once and are not synonyms or - hypernyms of the top name (these are included in responses)
- total_responses: Sum count of correct responses
- split: Use of the image in training vs. test vs. validation
- vg_object_id: The VisualGenome id of the object
- vg_image_id: The VisualGenome id of the image
- topname_agreement (only available for the test split): The number of responses for the top name divided by the number of total responses
- jaccard_similarity (only available for the test split): Jaccard similarity index of the responses column in CAT ManyNames and its human-annotated test set
### Data Splits
- Test: 1,072 images
- Val: 1,110 images
- Train: 21,503 images
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of multimodal models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
The original visual data comes from [VisualGenome](https://visualgenome.org/). The objects were categorized in the categories of people, animals_plants, vehicles, food, home, buildings, and clothing.
#### Who are the source language producers?
The original [ManyNames dataset](https://github.com/amore-upf/manynames).
### Annotations
Annotations for the CAT ManyNames were obtained by performing a machine translation of the ManyNames dataset, originally in English. The test set was humanly annotated.
#### Annotation process
[N/A]
#### Who are the annotators?
The human-annotated test set gathered 220 Catalan native volunteer participants.
### Personal and Sensitive Information
There is no sensitive information in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of multimodal models in Catalan, a low-resource language.
### Discussion of Biases
We have not applied any steps to reduce the impact of biases possibly present in the data.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Mar Domínguez Orfila (mar dot dominguez01 at estudiant dot upf dot edu)
### Licensing Information
CAT ManyNames is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@mastersthesis{MasterThesisMar,
author = "Mar Domínguez Orfila",
title = "I a això, com li dius? Generating a new dataset for object naming in Catalan through automatic translation",
school = "Universitat Pompeu Fabra",
year = 2022,
address = "Barcelona",
month = jul
}
``` |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-f9a2c1a2-7774983 | 2022-06-27T10:55:43.000Z | null | false | 878c8cb9d1166558036c1b3da41266e9fa599fe2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:lewtun/dog_food"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-f9a2c1a2-7774983/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- lewtun/dog_food
eval_info:
task: image_multi_class_classification
model: abhishek/convnext-tiny-finetuned-dogfood
metrics: ['matthews_correlation']
dataset_name: lewtun/dog_food
dataset_config: lewtun--dog_food
dataset_split: test
col_mapping:
image: image
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: abhishek/convnext-tiny-finetuned-dogfood
* Dataset: lewtun/dog_food
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-f9a2c1a2-7774984 | 2022-06-27T10:56:00.000Z | null | false | 63e660d3fa5ab454cc0ab1a253df2e0fccfac868 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:lewtun/dog_food"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-f9a2c1a2-7774984/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- lewtun/dog_food
eval_info:
task: image_multi_class_classification
model: douwekiela/resnet-18-finetuned-dogfood
metrics: ['matthews_correlation']
dataset_name: lewtun/dog_food
dataset_config: lewtun--dog_food
dataset_split: test
col_mapping:
image: image
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: douwekiela/resnet-18-finetuned-dogfood
* Dataset: lewtun/dog_food
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-f9a2c1a2-7774985 | 2022-06-27T10:56:06.000Z | null | false | 8052418bce17b26fdb4b05523c8367c98ec4f330 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:lewtun/dog_food"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-f9a2c1a2-7774985/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- lewtun/dog_food
eval_info:
task: image_multi_class_classification
model: sasha/swin-tiny-finetuned-dogfood
metrics: ['matthews_correlation']
dataset_name: lewtun/dog_food
dataset_config: lewtun--dog_food
dataset_split: test
col_mapping:
image: image
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: sasha/swin-tiny-finetuned-dogfood
* Dataset: lewtun/dog_food
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
CShorten | null | null | null | false | 1 | false | CShorten/Tweets-from-AK | 2022-07-12T21:53:20.000Z | null | false | 08e29bba8fed7e588839acf4022b2dcea84382d4 | [] | [] | https://huggingface.co/datasets/CShorten/Tweets-from-AK/resolve/main/README.md | This dataset contains Twitter information from AK92501 |
biglam | null | @dataset{van_strien_daniel_2021_5838410,
author = {van Strien, Daniel},
title = {{19th Century United States Newspaper Advert images
with 'illustrated' or 'non illustrated' labels}},
month = oct,
year = 2021,
publisher = {Zenodo},
version = {0.0.1},
doi = {10.5281/zenodo.5838410},
url = {https://doi.org/10.5281/zenodo.5838410}} | The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection. | false | 2 | false | biglam/illustrated_ads | 2022-09-13T10:07:51.000Z | null | false | 92e45a21733804d26aad08ad76b06e0174090303 | [] | [
"annotations_creators:expert-generated",
"license:cc0-1.0",
"size_categories:n<1K",
"tags:lam",
"tags:historic newspapers",
"task_categories:image-classification",
"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/biglam/illustrated_ads/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language: []
language_creators: []
license:
- cc0-1.0
multilinguality: []
pretty_name: 19th Century United States Newspaper Advert images with 'illustrated'
or 'non illustrated' labels
size_categories:
- n<1K
source_datasets: []
tags:
- lam
- historic newspapers
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
The Dataset contains images derived from the [Newspaper Navigator](news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/).
> [The Newspaper Navigator dataset](https://news-navigator.labs.loc.gov/) consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the Beyond Words crowdsourcing project. source: https://news-navigator.labs.loc.gov/
One of these categories is 'advertisements'. This dataset contains a sample of these images with additional labels indicating if the advert is 'illustrated' or 'not illustrated'.
This dataset was created for use in a [Programming Historian tutorial](http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt1). The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material.
# Dataset Card for 19th Century United States Newspaper Advert images with 'illustrated' or 'non illustrated' labels
## 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:**[https://doi.org/10.5281/zenodo.5838410](https://doi.org/10.5281/zenodo.5838410)
- **Paper:**[https://doi.org/10.46430/phen0101](https://doi.org/10.46430/phen0101)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Dataset contains images derived from the [Newspaper Navigator](news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/).
> [The Newspaper Navigator dataset](https://news-navigator.labs.loc.gov/) consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the Beyond Words crowdsourcing project. source: https://news-navigator.labs.loc.gov/
One of these categories is 'advertisements. This dataset contains a sample of these images with additional labels indicating if the advert is 'illustrated' or 'not illustrated'.
This dataset was created for use in a [Programming Historian tutorial](http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt1). The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material.
### Supported Tasks and Leaderboards
- `image-classification`: the primary purpose of this dataset is for classifying historic newspaper images identified as being 'advertisements' into 'illustrated' and 'not-illustrated' categories.
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
An example instance from this dataset
``` python
{'file': 'pst_fenske_ver02_data_sn84026497_00280776129_1880042101_0834_002_6_96.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=388x395 at 0x7F9A72038950>,
'label': 0,
'pub_date': Timestamp('1880-04-21 00:00:00'),
'page_seq_num': 834,
'edition_seq_num': 1,
'batch': 'pst_fenske_ver02',
'lccn': 'sn84026497',
'box': [0.649412214756012,
0.6045778393745422,
0.8002520799636841,
0.7152365446090698],
'score': 0.9609346985816956,
'ocr': "H. II. IIASLKT & SOXN, Dealers in General Merchandise In New Store Room nt HASLET'S COS ITERS, 'JTionoMtii, ln. .Tau'y 1st, 1?0.",
'place_of_publication': 'Tionesta, Pa.',
'geographic_coverage': "['Pennsylvania--Forest--Tionesta']",
'name': 'The Forest Republican. [volume]',
'publisher': 'Ed. W. Smiley',
'url': 'https://news-navigator.labs.loc.gov/data/pst_fenske_ver02/data/sn84026497/00280776129/1880042101/0834/002_6_96.jpg',
'page_url': 'https://chroniclingamerica.loc.gov/data/batches/pst_fenske_ver02/data/sn84026497/00280776129/1880042101/0834.jp2'}
```
### Data Fields
[More Information Needed]
### Data Splits
The dataset contains a single split.
## 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
A description of the annotation process is outlined in this [GitHub repository](https://github.com/Living-with-machines/nnanno)
[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
``` bibtex
@dataset{van_strien_daniel_2021_5838410,
author = {van Strien, Daniel},
title = {{19th Century United States Newspaper Advert images
with 'illustrated' or 'non illustrated' labels}},
month = oct,
year = 2021,
publisher = {Zenodo},
version = {0.0.1},
doi = {10.5281/zenodo.5838410},
url = {https://doi.org/10.5281/zenodo.5838410}}
```
[More Information Needed]
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
|
ju-resplande | null | \
#TODO: citation
} | \
#TODO: description | false | 1 | false | ju-resplande/askD | 2022-10-29T12:19:35.000Z | null | false | 7c595bcd1b0f21cba1280c14a549dc28a64e8114 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language_creators:machine-generated",
"language:en",
"language:pt",
"license:lgpl-3.0",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:extended|eli5",
"task_categories:... | https://huggingface.co/datasets/ju-resplande/askD/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
- machine-generated
language:
- en
- pt
license:
- lgpl-3.0
multilinguality:
- multilingual
- translation
size_categories:
- 100K<n<1M
source_datasets:
- extended|eli5
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- closed-domain-qa
pretty_name: AskDocs
---
# Dataset Card for askD
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/ju-resplande/askD
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[ELI5 dataset](https://huggingface.co/datasets/eli5) adapted on [Medical Questions (AskDocs)](https://www.reddit.com/r/AskDocs/) subreddit.
We additionally translated to Portuguese and used <a href="https://github.com/LasseRegin/medical-question-answer-data"> external data from here<a>.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language data in AskD is English (BCP-47 en) and Brazilian Portuguese (BCP-47 pt-BR)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
| | Train | Valid | Test | External |
| ----- | ------ | ----- | ---- | -------- |
| en | 24256 | 5198 | 5198 | 166804 |
| pt | 24256 | 5198 | 5198 | 166804 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The dataset questions and answers span a period from January 2013 to December 2019.
#### 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
```bibtex
@misc{Gomes20202,
author = {GOMES, J. R. S.},
title = {PLUE: Portuguese Language Understanding Evaluation},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ju-resplande/askD}},
commit = {42060c4402c460e174cbb75a868b429c554ba2b7}
}
```
### Contributions
Thanks to [@ju-resplande](https://github.com/ju-resplande) for adding this dataset. |
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