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150abc4c5faa537512149b9ed2bc675ec4e0413b
|
# esCorpius: A Massive Spanish Crawling Corpus
## Introduction
In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, the results in Spanish present important shortcomings, as they are either too small in comparison with other languages, or present a low quality derived from sub-optimal cleaning and deduplication. In this work, we introduce esCorpius, a Spanish crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in Spanish with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius has been released under CC BY-NC-ND 4.0 license.
## Statistics
| **Corpus** | OSCAR<br>22.01 | mC4 | CC-100 | ParaCrawl<br>v9 | esCorpius<br>(ours) |
|-------------------------|----------------|--------------|-----------------|-----------------|-------------------------|
| **Size (ES)** | 381.9 GB | 1,600.0 GB | 53.3 GB | 24.0 GB | 322.5 GB |
| **Docs (ES)** | 51M | 416M | - | - | 104M |
| **Words (ES)** | 42,829M | 433,000M | 9,374M | 4,374M | 50,773M |
| **Lang.<br>identifier** | fastText | CLD3 | fastText | CLD2 | CLD2 + fastText |
| **Elements** | Document | Document | Document | Sentence | Document and paragraph |
| **Parsing quality** | Medium | Low | Medium | High | High |
| **Cleaning quality** | Low | No cleaning | Low | High | High |
| **Deduplication** | No | No | No | Bicleaner | dLHF |
| **Language** | Multilingual | Multilingual | Multilingual | Multilingual | Spanish |
| **License** | CC-BY-4.0 | ODC-By-v1.0 | Common<br>Crawl | CC0 | CC-BY-NC-ND |
## Citation
Link to the paper: https://www.isca-speech.org/archive/pdfs/iberspeech_2022/gutierrezfandino22_iberspeech.pdf / https://arxiv.org/abs/2206.15147
Cite this work:
```
@inproceedings{gutierrezfandino22_iberspeech,
author={Asier Gutiérrez-Fandiño and David Pérez-Fernández and Jordi Armengol-Estapé and David Griol and Zoraida Callejas},
title={{esCorpius: A Massive Spanish Crawling Corpus}},
year=2022,
booktitle={Proc. IberSPEECH 2022},
pages={126--130},
doi={10.21437/IberSPEECH.2022-26}
}
```
## Disclaimer
We did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus.
|
LHF/escorpius
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:es",
"license:cc-by-nc-nd-4.0",
"arxiv:2206.15147",
"region:us"
] |
2022-06-24T19:58:40+00:00
|
{"language": ["es"], "license": "cc-by-nc-nd-4.0", "multilinguality": ["monolingual"], "size_categories": ["100M<n<1B"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"]}
|
2023-01-05T10:55:48+00:00
|
a8093a9c7757b59d64702f892002542e8f3a1fb0
|
# Dataset Card for GTSRB
## 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:** http://www.sciencedirect.com/science/article/pii/S0893608012000457
- **Repository:** https://github.com/bazylhorsey/gtsrb/
- **Paper:** Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
- **Leaderboard:** https://benchmark.ini.rub.de/gtsrb_results.html
- **Point of Contact:** bhorsey16@gmail.com
### Dataset Summary
The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties:
- Single-image, multi-class classification problem
- More than 40 classes
- More than 50,000 images in total
- Large, lifelike database
### Supported Tasks and Leaderboards
[Kaggle](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign) \
[Original](https://benchmark.ini.rub.de/gtsrb_results.html)
## Dataset Structure
### Data Instances
```
{
"Width": 31,
"Height": 31,
"Roi.X1": 6,
"Roi.Y1": 6,
"Roi.X2": 26,
"Roi.Y2": 26,
"ClassId": 20,
"Path": "Train/20/00020_00004_00002.png",
}
```
### Data Fields
- Width: width of image
- Height: Height of image
- Roi.X1: Upper left X coordinate
- Roi.Y1: Upper left Y coordinate
- Roi.X2: Lower right t X coordinate
- Roi.Y2: Lower right Y coordinate
- ClassId: Class of image
- Path: Path of image
### Data Splits
Categories: 42
Train: 39209
Test: 12630
## Dataset Creation
### Curation Rationale
Recognition of traffic signs is a challenging real-world problem of high industrial relevance. Although commercial systems have reached the market and several studies on this topic have been published, systematic unbiased comparisons of different approaches are missing and comprehensive benchmark datasets are not freely available.
Traffic sign recognition is a multi-class classification problem with unbalanced class frequencies. Traffic signs can provide a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. However, there exist subsets of classes (e. g., speed limit signs) that are very similar to each other.
The classifier has to cope with large variations in visual appearances due to illumination changes, partial occlusions, rotations, weather conditions, etc.
Humans are capable of recognizing the large variety of existing road signs with close to 100% correctness. This does not only apply to real-world driving, which provides both context and multiple views of a single traffic sign, but also to the recognition from single images.
<!-- ### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] -->
|
bazyl/GTSRB
|
[
"task_categories:image-classification",
"task_ids:multi-label-image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10K<n<100K",
"source_datasets:original",
"license:gpl-3.0",
"region:us"
] |
2022-06-24T23:30:19+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": [], "license": ["gpl-3.0"], "multilinguality": [], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-label-image-classification"], "pretty_name": "GTSRB"}
|
2022-10-25T09:39:19+00:00
|
17c2878bdcf8b76fd8fc626c61644b456875ef1f
|
Mithil/amazonFakeReview
|
[
"license:afl-3.0",
"region:us"
] |
2022-06-25T01:10:01+00:00
|
{"license": "afl-3.0"}
|
2022-06-25T01:12:18+00:00
|
|
b7ab718383f81b57ab16ebd780990265e234f79d
|
# AutoTrain Dataset for project: test_sum
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project test_sum.
### Languages
The BCP-47 code for the dataset's language is zh.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "7\u67086\u65e5\uff0c\u4e2d\u963f\u5408\u4f5c\u8bba\u575b\u7b2c\u4e5d\u5c4a\u90e8\u957f\u7ea7\u4f1a\u8bae\u56e0\u65b0\u51a0\u80ba\u708e\u75ab\u60c5\u4ee5\u89c6\u9891\u8fde\u7ebf\u65b9\u5f0f\u4e3e\u884c\u3002\n\u672c\u5c4a\u4f1a\u8bae\u53d6\u5f97\u4e86\u5706\u6ee1\u6210\u529f\uff0c\u53d1\u8868\u4e09\u4efd\u6210\u679c\u6587\u4ef6\uff0c\u9ad8\u5ea6\u51dd\u805a\u4e2d\u963f\u5171\u8bc6\u3002\u300a\u4e2d\u56fd\u548c\u963f\u62c9\u4f2f\u56fd\u5bb6\u56e2\u7ed3\u6297\u51fb\u65b0\u51a0\u80ba\u708e\u75ab\u60c5\u8054\u5408\u58f0\u660e\u300b\u5c55\u73b0\u4e86\u4e2d\u963f\u6218\u80dc\u75ab\u60c5[...]",
"target": "\u671b\u6d77\u697c\u52a0\u5f3a\u5408\u4f5c\u5171\u514b\u65f6\u8270\u643a\u624b\u524d\u884c"
},
{
"text": "\u4e60\u8fd1\u5e73\u603b\u4e66\u8bb0\u6307\u51fa\uff1a\u201c\u6293\u4f4f\u4e86\u521b\u65b0\uff0c\u5c31\u6293\u4f4f\u4e86\u7275\u52a8\u7ecf\u6d4e\u793e\u4f1a\u53d1\u5c55\u5168\u5c40\u7684\u2018\u725b\u9f3b\u5b50\u2019\u3002\u201d\u201c\u8c01\u5728\u521b\u65b0\u4e0a\u5148\u884c\u4e00\u6b65\uff0c\u8c01\u5c31\u80fd\u62e5\u6709\u5f15\u9886\u53d1\u5c55\u7684\u4e3b\u52a8\u6743\u3002\u201d\n\u6293\u521b\u65b0\u5c31\u662f\u6293\u53d1\u5c55\uff0c\u8c0b\u521b\u65b0\u5c31\u662f\u8c0b\u672a\u6765\u3002\u5317\u4eac\u9ad8\u6807\u51c6\u63a8\u8fdb\u201c\u4e24\u533a\u201d\u5efa\u8bbe\uff0c\u6838\u5fc3\u4efb[...]",
"target": "\u6293\u521b\u65b0\u5c31\u662f\u6293\u53d1\u5c55"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1343 |
| valid | 336 |
|
pcy/autotrain-data-test_sum
|
[
"language:zh",
"region:us"
] |
2022-06-25T01:19:32+00:00
|
{"language": ["zh"], "task_categories": ["conditional-text-generation"]}
|
2022-10-23T05:18:13+00:00
|
b7b32323718ea1811372e7dd85079d4f0be1f16c
|
## Dataset Description
- **Size of downloaded dataset files:** 126 MB
This dataset contains the exegeses/tafsirs (تفسير القرآن) of the holy Quran in arabic by 8 exegetes.
This is a non Official dataset. It have been scrapped from the `Quran.com Api`
This dataset contains `49888` records with `+14` Million words. `8` records per Quranic verse
Usage Example :
```python
from datasets import load_dataset
tafsirs = load_dataset("mustapha/QuranExe")
```
|
mustapha/QuranExe
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:sentence-similarity",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:mit",
"region:us"
] |
2022-06-25T06:07:28+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["ar"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask", "sentence-similarity"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "QuranExe"}
|
2022-07-20T14:33:24+00:00
|
9ecd0450d4ce5378973825ae2f93e15648c0da3d
|
# 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: autoevaluate/multi-class-classification
* Dataset: emotion
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
|
autoevaluate/autoeval-staging-eval-project-5ece7d74-70d9-4701-a9b7-1777e66ed4b0-5145
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T07:04:54+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "autoevaluate/multi-class-classification", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2022-06-25T07:05:40+00:00
|
4c8baf4b8f039e38a101b9e18ac1c7c5b3cc7a51
|
# 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: autoevaluate/multi-class-classification
* Dataset: emotion
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
|
autoevaluate/autoeval-staging-eval-project-bba54b81-5330-48f8-b7bf-1cb797f93bcf-5246
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T07:16:25+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "autoevaluate/multi-class-classification", "metrics": ["matthews_correlation"], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2022-06-25T07:17:13+00:00
|
8466e829412dd77cd4bd6d7ff5b17176bcb68bff
|
# 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: autoevaluate/binary-classification
* Dataset: glue
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
|
autoevaluate/autoeval-staging-eval-project-21811dfd-a09c-4692-82b2-7e358a2520ce-5347
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T07:26:01+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "binary_classification", "model": "autoevaluate/binary-classification", "dataset_name": "glue", "dataset_config": "sst2", "dataset_split": "validation", "col_mapping": {"text": "sentence", "target": "label"}}}
|
2022-06-25T07:26:38+00:00
|
b9b11cf76caa251ce544c1567b8f1af8be4dc04e
|
# 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: autoevaluate/binary-classification
* 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/autoeval-staging-eval-project-840224bd-ff8b-4526-8827-e12d96f6c7bf-5448
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T07:33:38+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "binary_classification", "model": "autoevaluate/binary-classification", "metrics": ["matthews_correlation"], "dataset_name": "glue", "dataset_config": "sst2", "dataset_split": "validation", "col_mapping": {"text": "sentence", "target": "label"}}}
|
2022-06-25T07:34:15+00:00
|
e60de7b9cf5a2e12c9321c6a1f012d929869c05f
|
# 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: autoevaluate/image-multi-class-classification
* Dataset: autoevaluate/mnist-sample
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/autoeval-staging-eval-project-896d78da-9e5e-4706-b736-32d4a31ff571-5549
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T07:39:44+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["autoevaluate/mnist-sample"], "eval_info": {"task": "image_multi_class_classification", "model": "autoevaluate/image-multi-class-classification", "metrics": ["matthews_correlation"], "dataset_name": "autoevaluate/mnist-sample", "dataset_config": "autoevaluate--mnist-sample", "dataset_split": "test", "col_mapping": {"image": "image", "target": "label"}}}
|
2022-06-25T07:40:11+00:00
|
1cc3c98dba3490e9baf21032dbb0e22478bd021d
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: autoevaluate/translation
* Dataset: wmt16
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/autoeval-staging-eval-project-6715a17f-ec96-4660-9a86-49fe175a04f1-5650
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T07:44:55+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["wmt16"], "eval_info": {"task": "translation", "model": "autoevaluate/translation", "metrics": [], "dataset_name": "wmt16", "dataset_config": "ro-en", "dataset_split": "test", "col_mapping": {"source": "translation.ro", "target": "translation.en"}}}
|
2022-06-25T07:48:52+00:00
|
f2c69440251afcf9073cf02763f78d5e4028c80c
|
# 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: autoevaluate/summarization
* Dataset: xsum
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/autoeval-staging-eval-project-62ca8f86-389e-4833-9ccf-a97cadcf4874-5751
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T07:52:42+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "autoevaluate/summarization", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2022-06-25T07:59:10+00:00
|
221a5d6d5803b7c47bb4ffce4ea06e14472e156b
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
autoevaluate/squad-sample
|
[
"region:us"
] |
2022-06-25T07:56:36+00:00
|
{}
|
2023-06-29T13:50:34+00:00
|
dcd8aacae4514b44aae68d36afdc61a22ef98534
|
# 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: transformersbook/xlm-roberta-base-finetuned-panx-all
* 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.
|
autoevaluate/autoeval-staging-eval-project-fed20ca6-7444804
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T08:22:09+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["wikiann"], "eval_info": {"task": "entity_extraction", "model": "transformersbook/xlm-roberta-base-finetuned-panx-all", "metrics": ["matthews_correlation"], "dataset_name": "wikiann", "dataset_config": "en", "dataset_split": "test", "col_mapping": {"tokens": "tokens", "tags": "ner_tags"}}}
|
2022-06-25T08:25:01+00:00
|
b076ba7227761f3e25116ea7b40f0cb0115d946e
|
# 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: andi611/distilbert-base-uncased-ner-agnews
* 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/autoeval-staging-eval-project-17e9fcc1-7454805
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T08:33:35+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["ag_news"], "eval_info": {"task": "multi_class_classification", "model": "andi611/distilbert-base-uncased-ner-agnews", "metrics": [], "dataset_name": "ag_news", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2022-06-25T08:34:15+00:00
|
cbc9a1fccd0d5c7e84ca53b2c5744ec75e4ce334
|
# 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: mrm8488/distilroberta-finetuned-age_news-classification
* 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/autoeval-staging-eval-project-17e9fcc1-7454810
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-25T08:34:17+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["ag_news"], "eval_info": {"task": "multi_class_classification", "model": "mrm8488/distilroberta-finetuned-age_news-classification", "metrics": [], "dataset_name": "ag_news", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2022-06-25T08:35:01+00:00
|
e3572e08c43f909c626027c39c7b56c8b8ee5bef
|
loubnabnl/github-clean
|
[
"license:apache-2.0",
"region:us"
] |
2022-06-25T10:36:42+00:00
|
{"license": "apache-2.0"}
|
2022-06-25T10:36:42+00:00
|
|
eebfc857e775f10513dd739c355e326937d58de9
|
# 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).
|
sophieb/dynamically_generated_hate_speech_dataset
|
[
"region:us"
] |
2022-06-25T16:48:05+00:00
|
{}
|
2022-06-25T17:02:18+00:00
|
a541059116085f520b549ab6572c65a06086d445
|
peabits/a09
|
[
"license:apache-2.0",
"region:us"
] |
2022-06-26T02:03:46+00:00
|
{"license": "apache-2.0"}
|
2022-06-26T02:03:46+00:00
|
|
4f6a54fc39110a7b3289c12f58122ead7cf5dcbb
|
# 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
|
malaysia-ai/crawl-cambridge-english-malaysian
|
[
"language:ms",
"region:us"
] |
2022-06-26T04:56:04+00:00
|
{"language": "ms"}
|
2022-10-15T08:33:19+00:00
|
c31bd6bbb3460267ae1da555b9804579a2f99e01
|
# 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.
|
lewtun/dog_food
|
[
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] |
2022-06-26T06:50:59+00:00
|
{"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"], "pretty_name": "Dog vs Food Dataset"}
|
2022-07-03T04:15:18+00:00
|
3210c118b2c5921129ee63869e0804d025a083e8
|
# 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/autoeval-staging-eval-project-e1907042-7494827
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:25:25+00:00
|
{"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"}}}
|
2022-06-26T10:26:03+00:00
|
139dd68cf257ce2ea6f78625384d235ce98cb474
|
# 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/autoeval-staging-eval-project-e1907042-7494828
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:25:27+00:00
|
{"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"}}}
|
2022-06-26T10:27:08+00:00
|
d00db7288fa1c5448ef448afaa079ee7fc723869
|
# 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/autoeval-staging-eval-project-e1907042-7494829
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:25:34+00:00
|
{"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"}}}
|
2022-06-26T10:27:14+00:00
|
308091a601bcff02e6b72cfab2dec043721ca47a
|
# 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/autoeval-staging-eval-project-e1907042-7494830
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:25:38+00:00
|
{"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"}}}
|
2022-06-26T10:26:14+00:00
|
79343efea08c58d4cb5aaa3377515681e17b8e84
|
# 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/autoeval-staging-eval-project-e1907042-7494831
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:25:44+00:00
|
{"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"}}}
|
2022-06-26T10:26:20+00:00
|
af158837c25078f3a6881133f350a87ced485365
|
# 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/autoeval-staging-eval-project-e1907042-7494832
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:25:49+00:00
|
{"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"}}}
|
2022-06-26T10:26:25+00:00
|
089d3602f57afc6948cb926890662f5e190d8a1f
|
# 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/autoeval-staging-eval-project-e1907042-7494835
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:26:07+00:00
|
{"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"}}}
|
2022-06-26T10:26:45+00:00
|
523a4305c0e595c344b4d85572ba852e86042b19
|
# 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/autoeval-staging-eval-project-e1907042-7494833
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:26:09+00:00
|
{"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"}}}
|
2022-06-26T10:29:12+00:00
|
dcb235a5378155bc061bdceb73205c4308806a7a
|
# 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/autoeval-staging-eval-project-e1907042-7494836
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:26:13+00:00
|
{"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"}}}
|
2022-06-26T10:26:51+00:00
|
8570ba48a386626e7cfd4e551dfe622f8b841a34
|
# 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.
|
autoevaluate/autoeval-staging-eval-project-e1907042-7494834
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T10:26:18+00:00
|
{"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"}}}
|
2022-06-26T10:29:24+00:00
|
5b6da7cd5381ce0a79e7faa2bfd9ad18372abac7
|
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
|
ElKulako/stocktwits-crypto
|
[
"region:us"
] |
2022-06-26T15:05:24+00:00
|
{}
|
2022-08-31T23:46:26+00:00
|
b7db0a9cdf3c918e10f834240dc69f3bb68c3166
|
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"
|
rjac/all-the-news-2-1-Component-one-embedding
|
[
"region:us"
] |
2022-06-26T15:37:22+00:00
|
{}
|
2022-07-18T17:09:59+00:00
|
12e1d31710ba2c6b4a173fdd1c54504e27b81747
|
This repo contains datasets for our paper.
|
SoDehghan/datasets_for_supmpn
|
[
"license:apache-2.0",
"region:us"
] |
2022-06-26T17:44:25+00:00
|
{"license": "apache-2.0"}
|
2022-06-26T17:50:11+00:00
|
736fe56ff6f740a268dd379d455006b4abfac49d
|
# 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/autoeval-staging-eval-project-0839fa4f-7534859
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T18:41:19+00:00
|
{"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"}}}
|
2022-06-26T18:42:20+00:00
|
58e47aa8905bb969ba88b7fdd3afdb60bce83959
|
# 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/autoeval-staging-eval-project-d05a5ffd-7544860
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T18:42:27+00:00
|
{"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"}}}
|
2022-06-26T18:45:06+00:00
|
7b4c75012546212bf38f998f18fb697107201a2e
|
# 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/autoeval-staging-eval-project-d05a5ffd-7544861
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T18:42:27+00:00
|
{"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"}}}
|
2022-06-26T18:43:02+00:00
|
5d1738163151b8e352a623484b42035d01da5fae
|
# 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/autoeval-staging-eval-project-3aabac9e-7554863
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T18:47:32+00:00
|
{"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"}}}
|
2022-06-26T18:58:52+00:00
|
08ef47c03de533bc48e17d7e2b5b0517976a5e9b
|
# 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/autoeval-staging-eval-project-3aabac9e-7554868
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T18:47:57+00:00
|
{"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"}}}
|
2022-06-26T19:58:35+00:00
|
ea4825976f3f6de4b1c52d0764e2f8efb3f79a55
|
# 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/autoeval-staging-eval-project-3aabac9e-7554869
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T18:48:03+00:00
|
{"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"}}}
|
2022-06-26T18:57:41+00:00
|
5c91d83eea059c858167c9d7aba66ce1f6bd72f6
|
# 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.
|
autoevaluate/autoeval-staging-eval-project-2f2d3a43-7564875
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T18:54:08+00:00
|
{"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"}}}
|
2022-06-26T18:56:39+00:00
|
73083debe3671f42d430d9c0ad660a4fca88796c
|
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.
|
rjac/all-the-news-2-1-Component-one-sentence-embedding
|
[
"region:us"
] |
2022-06-26T18:55:48+00:00
|
{}
|
2022-06-27T11:31:21+00:00
|
73c75a3f2fc58422fcf0d555b397bd578dcf4992
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574879
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:11+00:00
|
{"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"}}}
|
2022-06-26T19:12:39+00:00
|
26adeb3081069e4817f63dcb62b1eb9140baafaa
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574881
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:18+00:00
|
{"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"}}}
|
2022-06-26T19:10:59+00:00
|
727df2a9c9c11c25d79f06ceaea6c8724fee5bfc
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574882
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:25+00:00
|
{"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"}}}
|
2022-06-26T19:11:28+00:00
|
a4bba63772c141cb9d4a8f9a7b78063afac563a3
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574880
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:26+00:00
|
{"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"}}}
|
2022-06-26T19:13:47+00:00
|
d888fc3c84f9de412ac8d81cc2d35aba646d1843
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574883
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:30+00:00
|
{"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"}}}
|
2022-06-26T19:11:34+00:00
|
3d278a75960eb211a6e7a141630d9d1425bf602b
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574884
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:35+00:00
|
{"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"}}}
|
2022-06-26T19:11:24+00:00
|
aeaef0d1cf99758936dfbd65179f38ef512a8053
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574885
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:41+00:00
|
{"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"}}}
|
2022-06-26T19:11:28+00:00
|
9c5d7d1cc44e74d0141da4452133f7dfabf08209
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574886
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:48+00:00
|
{"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"}}}
|
2022-06-26T19:11:34+00:00
|
b7b163b11963acee53bdfe80ff06235c76b14119
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574887
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:54+00:00
|
{"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"}}}
|
2022-06-26T19:11:40+00:00
|
df3f776ab930859fdbaa0be25c9dac5dd02611f4
|
# 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/autoeval-staging-eval-project-d60b4e7e-7574888
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:08:59+00:00
|
{"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"}}}
|
2022-06-26T19:11:48+00:00
|
9d50a177394677964a26dd2ecfbbc1530830d0c6
|
# 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/autoeval-staging-eval-project-bc0462a6-7584891
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:10:06+00:00
|
{"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"}}}
|
2022-06-26T19:12:49+00:00
|
67111d7eec91e1444ae992f6634227a5e84c8f47
|
# 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/autoeval-staging-eval-project-bc0462a6-7584893
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:10:34+00:00
|
{"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"}}}
|
2022-06-26T19:13:17+00:00
|
6c2d6b0fa7b1a83828c1c24bb4a3d0bf0a5118e9
|
# 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/autoeval-staging-eval-project-bc0462a6-7584895
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:10:34+00:00
|
{"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"}}}
|
2022-06-26T19:13:18+00:00
|
21052961d6b846097b36bafb01a63a832a6e5c91
|
# 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/autoeval-staging-eval-project-0a15404e-7594901
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:12:10+00:00
|
{"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"}}}
|
2022-06-26T19:15:10+00:00
|
4f3c3daa391f4a5fabea115a410648e57832bb7a
|
# 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/autoeval-staging-eval-project-d578e0ca-7604911
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:14:01+00:00
|
{"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"}}}
|
2022-06-26T19:16:39+00:00
|
b2a38440e30ffb12f0b4279d49293bf77b59311f
|
# 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/autoeval-staging-eval-project-019e0f0d-7644945
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:21:02+00:00
|
{"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"}}}
|
2022-06-26T22:46:29+00:00
|
ae50b1ecd96a469dd52c3d1b37c31dd2a996491e
|
# 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/autoeval-staging-eval-project-d47ba8c2-7654948
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:22:09+00:00
|
{"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"}}}
|
2022-06-26T22:44:04+00:00
|
3b1f469369c1cfdf469976e8b5f361f914bfe965
|
# 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/autoeval-staging-eval-project-d47ba8c2-7654949
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:22:15+00:00
|
{"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"}}}
|
2022-06-26T22:45:21+00:00
|
fc98e2a9feddabebccaf376bea6e5f94473c8537
|
# 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/autoeval-staging-eval-project-0b0f26eb-7664950
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:31:37+00:00
|
{"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"}}}
|
2022-06-26T19:32:34+00:00
|
c04d5f52a828fa57fc798a4a4b79ee9e82d52940
|
# 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/autoeval-staging-eval-project-0b0f26eb-7664951
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:31:58+00:00
|
{"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"}}}
|
2022-06-26T19:35:49+00:00
|
1de7c600000a6d8186cec9965df97910ae72c28c
|
# 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/autoeval-staging-eval-project-6971abf9-7684954
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:36:34+00:00
|
{"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"}}}
|
2022-06-26T19:37:24+00:00
|
862bba82d3069ff7c2652f7de36248e067159363
|
# 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/autoeval-staging-eval-project-6971abf9-7684956
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:36:43+00:00
|
{"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"}}}
|
2022-06-26T19:37:31+00:00
|
0465942446b2c98a9f5efac91edc4380aa3247b4
|
# 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/autoeval-staging-eval-project-6971abf9-7684957
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:36:48+00:00
|
{"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"}}}
|
2022-06-26T19:37:33+00:00
|
702c383a751576b6d21051dff9ea8af71a5f0e9b
|
# 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/autoeval-staging-eval-project-6971abf9-7684955
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:36:51+00:00
|
{"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"}}}
|
2022-06-26T19:39:59+00:00
|
fc5a4b24d3683be8d9c183c07baf1bb65b1b6980
|
# 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/autoeval-staging-eval-project-200453bd-7694959
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:37:29+00:00
|
{"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"}}}
|
2022-06-26T19:38:03+00:00
|
52bfa7c79752f42400bae49ff02eb0e159934e67
|
# 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/autoeval-staging-eval-project-200453bd-7694960
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:37:35+00:00
|
{"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"}}}
|
2022-06-26T19:38:14+00:00
|
e75123b7e46a48a69278563d09d968f51eebde56
|
# 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/autoeval-staging-eval-project-200453bd-7694961
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:37:41+00:00
|
{"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"}}}
|
2022-06-26T19:38:19+00:00
|
fc0435887fc54bc703347b13c25ac39fb3413217
|
# 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/autoeval-staging-eval-project-200453bd-7694962
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:37:48+00:00
|
{"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"}}}
|
2022-06-26T19:38:35+00:00
|
43d7585b40d2dcb9ffa5c80e2d42ac2f634c2875
|
# 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/autoeval-staging-eval-project-200453bd-7694963
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:37:53+00:00
|
{"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"}}}
|
2022-06-26T19:38:38+00:00
|
ba02b413d1217559c3274f72751aa3efd0956470
|
# 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/autoeval-staging-eval-project-200453bd-7694964
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:38:01+00:00
|
{"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"}}}
|
2022-06-26T19:39:11+00:00
|
430862f84254922abed2ff52371c235f27b92fe8
|
# 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/autoeval-staging-eval-project-200453bd-7694965
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:38:06+00:00
|
{"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"}}}
|
2022-06-26T19:38:53+00:00
|
bd322d79fe53c69df0e3d268681abc99e03ffbda
|
# 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/autoeval-staging-eval-project-200453bd-7694966
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:38:13+00:00
|
{"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"}}}
|
2022-06-26T19:38:59+00:00
|
2bed5936e172bfe81d38c91685f0ecb62bed5a2a
|
# 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/autoeval-staging-eval-project-ab647f27-7704968
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:38:25+00:00
|
{"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"}}}
|
2022-06-26T19:39:12+00:00
|
4132c26b1e8d26dd8f566b19b7282004302c38ae
|
# 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/autoeval-staging-eval-project-ab647f27-7704969
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:38:31+00:00
|
{"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"}}}
|
2022-06-26T19:39:18+00:00
|
090405d02d57968300ce05415d1df3cce1db7cee
|
# 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/autoeval-staging-eval-project-ab647f27-7704970
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:38:38+00:00
|
{"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"}}}
|
2022-06-26T19:39:27+00:00
|
3040aff916ba4284b4324e9e19756c309739387f
|
# 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.
|
autoevaluate/autoeval-staging-eval-project-ab647f27-7704971
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-26T19:38:46+00:00
|
{"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"}}}
|
2022-06-26T19:39:32+00:00
|
2f3638e6fe9986450163567e06c757188bc54991
|
charlesmichaelvaughn/charlesmichaelvaughn
|
[
"license:apache-2.0",
"region:us"
] |
2022-06-27T03:53:23+00:00
|
{"license": "apache-2.0"}
|
2022-06-27T03:53:23+00:00
|
|
a1b5eceac63a5f3e471a798b79397d3c33b4a962
|
lamarvandusen/lamarvandusen
|
[
"license:apache-2.0",
"region:us"
] |
2022-06-27T05:14:36+00:00
|
{"license": "apache-2.0"}
|
2022-06-27T05:14:36+00:00
|
|
9e2d4bbce448f22bb4428131a02a400357ef4301
|
# 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.
|
autoevaluate/autoeval-staging-eval-project-8ef742e5-7734972
|
[
"autotrain",
"evaluation",
"region:us"
] |
2022-06-27T06:46:57+00:00
|
{"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"}}}
|
2022-06-27T06:48:40+00:00
|
ee1cf239fa0b2d617d2224b29ea031a0686200ff
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/4?source=Huggingface
- **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://www.nexdata.ai/datasets/4?source=Huggingface
### 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
|
Nexdata/Human_Face_Image_Data_with_Multiple_Angles_Light_Conditions_and_Expressions
|
[
"region:us"
] |
2022-06-27T07:09:31+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:47:14+00:00
|
0a17df29dc07b7b223f45f2e837dd25ab8625639
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/9?source=Huggingface
- **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://www.nexdata.ai/datasets/9?source=Huggingface
### 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
|
Nexdata/Multi-pose_and_Multi-expression_Face_Data
|
[
"region:us"
] |
2022-06-27T07:11:16+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:43:18+00:00
|
37a8c936e9306992853488422bac87d7e544e69c
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/963?source=Huggingface
- **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://www.nexdata.ai/datasets/963?source=Huggingface
### 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
|
Nexdata/Driver_Behavior_Collection_Data
|
[
"region:us"
] |
2022-06-27T07:12:41+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T09:59:35+00:00
|
ac3769601172b2fea46ddfa3034ee94d13a06842
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1134?source=Huggingface
- **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://www.nexdata.ai/datasets/1134?source=Huggingface
### 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
|
Nexdata/Infrared_Face_Recognition_Data
|
[
"region:us"
] |
2022-06-27T07:14:00+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:46:48+00:00
|
5004190602710a2ba9d6eeafe57b1b12e7dba017
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1083?source=Huggingface
- **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://www.nexdata.ai/datasets/1083?source=Huggingface
### 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
|
Nexdata/Passenger_Behavior_Recognition_Data
|
[
"region:us"
] |
2022-06-27T07:15:49+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T10:07:27+00:00
|
11c37a8583b1337c08242b33f32fb9e411df5601
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1075?source=Huggingface
- **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://www.nexdata.ai/datasets/1075?source=Huggingface
### 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
|
Nexdata/Multi-race_Driver_Behavior_Collection_Data
|
[
"region:us"
] |
2022-06-27T07:17:17+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T10:09:55+00:00
|
92147215b501acf991ffc69b118c5ec9f948821b
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1084?source=Huggingface
- **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://www.nexdata.ai/datasets/1084?source=Huggingface
### 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
|
Nexdata/Face_Recognition_Data_with_Gauze_Mask
|
[
"region:us"
] |
2022-06-27T07:18:32+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:46:17+00:00
|
e7be2465ab9506887c641ef67a19e90ae0cb4009
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1073?source=Huggingface
- **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://www.nexdata.ai/datasets/1073?source=Huggingface
### 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
|
Nexdata/Occluded_and_Multi-pose_Face_Recognition_Data
|
[
"region:us"
] |
2022-06-27T07:20:04+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:42:06+00:00
|
902793bb08eb7de8a474647ea060bc97094dcd3b
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/127?source=Huggingface
- **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://www.nexdata.ai/datasets/127?source=Huggingface
### 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
|
Nexdata/Handwriting_OCR_Data_of_Japanese_and_Korean
|
[
"region:us"
] |
2022-06-27T07:21:39+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T10:10:13+00:00
|
d9358a68869d60e99f11bdae3ba06ff2bcea99cb
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1064?source=Huggingface
- **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://www.nexdata.ai/datasets/1064?source=Huggingface
### 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
|
Nexdata/Natural_Scenes_OCR_Data_of_12_Languages
|
[
"region:us"
] |
2022-06-27T07:23:57+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:15:54+00:00
|
aad387265d200bd64aafea9bdd2c5a34c0eb6ba0
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/971?source=Huggingface
- **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://www.nexdata.ai/datasets/971?source=Huggingface
### 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
|
Nexdata/Living_Face_Anti-Spoofing_Data
|
[
"region:us"
] |
2022-06-27T07:38:04+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T10:00:01+00:00
|
ea37807798788158e8aeea84e243004a4b14ce61
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1089?source=Huggingface
- **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://www.nexdata.ai/datasets/1089?source=Huggingface
### 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
|
Nexdata/3D_Living_Face_Anti_Spoofing_Data
|
[
"region:us"
] |
2022-06-27T07:42:33+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T10:00:53+00:00
|
5a870df7041aa538bee06dd4ece2dddd926c44a1
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/973?source=Huggingface
- **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://www.nexdata.ai/datasets/973?source=Huggingface
### 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
|
Nexdata/Multi-race_7_Expressions_Recognition_Data
|
[
"region:us"
] |
2022-06-27T07:43:50+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T10:06:01+00:00
|
921c5035748e32774042b7b4a9e4676af2c94295
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/972?source=Huggingface
- **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://www.nexdata.ai/datasets/972?source=Huggingface
### 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
|
Nexdata/50_Types_of_Dynamic_Gesture_Recognition_Data
|
[
"region:us"
] |
2022-06-27T07:45:20+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:41:14+00:00
|
2b05c42473e7f983ebbae7efbc1c446f2c754749
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1016?source=Huggingface
- **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://www.nexdata.ai/datasets/1016?source=Huggingface
### 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
|
Nexdata/Multi-race_and_Multi-pose_Face_Images_Data
|
[
"region:us"
] |
2022-06-27T07:49:18+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T10:05:43+00:00
|
615fba6713eb0e6abd1cdc14d0fb4a714a5725a7
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1040?source=Huggingface
- **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://www.nexdata.ai/datasets/1040?source=Huggingface
### 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
|
Nexdata/3D_Instance_Segmentation_and_22_Landmarks_Annotation_Data_of_Human_Body
|
[
"region:us"
] |
2022-06-27T07:52:04+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:47:41+00:00
|
39c2168d68066762075fc8e1b89cda7ddb424294
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1052?source=Huggingface
- **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://www.nexdata.ai/datasets/1052?source=Huggingface
### 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
|
Nexdata/Human_Facial_Skin_Defects_Data
|
[
"region:us"
] |
2022-06-27T07:53:34+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:40:21+00:00
|
1c2e8ad09ad62fe92e9ce9dd9e0acd3b0617748b
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1057?source=Huggingface
- **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://www.nexdata.ai/datasets/1057?source=Huggingface
### 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
|
Nexdata/Multi-class_Fashion_Item_Detection_Data
|
[
"region:us"
] |
2022-06-27T07:54:36+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2024-02-04T10:08:44+00:00
|
7c9dfd67fd3763e0f98a5b8e10bd2ff239c55f50
|
# Dataset Card for Nexdata/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://www.nexdata.ai/datasets/1093?source=Huggingface
- **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://www.nexdata.ai/datasets/1093?source=Huggingface
### 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
|
Nexdata/3D_Face_Recognition_Images_Data
|
[
"region:us"
] |
2022-06-27T07:55:51+00:00
|
{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}
|
2023-08-31T01:45:01+00:00
|
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