Upload batch 85 (20 files, last=huggingface_dataset/Dataset_Card/morgan_tortas.md)

huggingface_dataset/Dataset_Card/Datatang_French_Speech_Data_by_Mobile_Phone_Reading.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for Datatang/French_Speech_Data_by_Mobile_Phone_Reading
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://bit.ly/3HJr94X
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The data volume is 231 hours and is recorded by 406 speakers (from French, Canada, and Africa). The recording is in quiet environment and rich in content. It contains various fields like economics, entertainment, news, and spoken language. All texts are manually transcribed. The sentence accuracy rate is 95%.
+                           
+For more details, please refer to the link: https://bit.ly/3HJr94X
+
+### Supported Tasks and Leaderboards
+
+automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
+
+### Languages
+
+French
+## 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

huggingface_dataset/Dataset_Card/Parmann_speech_classification.md

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+This dataset contains MFCC feature extracted for 646 short speech audios

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-squad_v2-squad_v2-8571ec-1652758611.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: SupriyaArun/bert-base-uncased-finetuned-squad
+  metrics: []
+  dataset_name: squad_v2
+  dataset_config: squad_v2
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: SupriyaArun/bert-base-uncased-finetuned-squad
+* Dataset: squad_v2
+* Config: squad_v2
+* Split: validation
+
+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.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-019e0f0d-7644945.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- scientific_papers
+eval_info:
+  task: summarization
+  model: google/bigbird-pegasus-large-pubmed
+  metrics: []
+  dataset_name: scientific_papers
+  dataset_config: pubmed
+  dataset_split: test
+  col_mapping:
+    text: article
+    target: abstract
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: google/bigbird-pegasus-large-pubmed
+* Dataset: scientific_papers
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-0919d128-ac07-4f9a-b929-706957da9f2e-4341.md

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+---
+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
+---
+# 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
+* Config: default
+* Split: test
+
+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.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-6a6944f2-7244759.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- wikiann
+eval_info:
+  task: entity_extraction
+  model: transformersbook/xlm-roberta-base-finetuned-panx-all
+  dataset_name: wikiann
+  dataset_config: en
+  dataset_split: test
+  col_mapping:
+    tokens: tokens
+    tags: ner_tags
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: transformersbook/xlm-roberta-base-finetuned-panx-all
+* Dataset: wikiann
+
+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.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-e438add5-1e56-41ec-9c26-2ad4182383b0-6260.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- autoevaluate/squad-sample
+eval_info:
+  task: extractive_question_answering
+  model: autoevaluate/extractive-question-answering
+  metrics: []
+  dataset_name: autoevaluate/squad-sample
+  dataset_config: autoevaluate--squad-sample
+  dataset_split: test
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: autoevaluate/extractive-question-answering
+* Dataset: autoevaluate/squad-sample
+* Config: autoevaluate--squad-sample
+* Split: test
+
+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.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-imdb-f49f2e4f-12435655.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- imdb
+eval_info:
+  task: binary_classification
+  model: lvwerra/distilbert-imdb
+  metrics: []
+  dataset_name: imdb
+  dataset_config: plain_text
+  dataset_split: test
+  col_mapping:
+    text: text
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Binary Text Classification
+* Model: lvwerra/distilbert-imdb
+* Dataset: imdb
+* Config: plain_text
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lvwerra](https://huggingface.co/lvwerra) for evaluating this model.

huggingface_dataset/Dataset_Card/clips_mfaq.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- other
+language:
+- cs
+- da
+- de
+- en
+- es
+- fi
+- fr
+- he
+- hr
+- hu
+- id
+- it
+- nl
+- 'no'
+- pl
+- pt
+- ro
+- ru
+- sv
+- tr
+- vi
+license:
+- cc0-1.0
+multilinguality:
+- multilingual
+pretty_name: MFAQ - a Multilingual FAQ Dataset
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- multiple-choice-qa
+---
+# MFAQ
+
+🚨 See [MQA](https://huggingface.co/datasets/clips/mqa) or [MFAQ Light](maximedb/mfaq_light) for an updated version of the dataset.
+
+MFAQ is a multilingual corpus of *Frequently Asked Questions* parsed from the [Common Crawl](https://commoncrawl.org/).
+```
+from datasets import load_dataset
+load_dataset("clips/mfaq", "en")
+{
+  "qa_pairs": [
+    {
+      "question": "Do  I need a rental Car in Cork?",
+      "answer": "If you plan on travelling outside of Cork City, for instance to  Kinsale [...]"
+    },
+    ...
+  ]
+}
+```
+
+## Languages
+We collected around 6M pairs of questions and answers in 21 different languages. To download a language specific subset you need to specify the language key as configuration. See below for an example.
+```
+load_dataset("clips/mfaq", "en") # replace "en" by any language listed below
+```
+
+| Language   | Key | Pairs     | Pages     |
+|------------|-----|-----------|-----------|
+| All        | all | 6,346,693 | 1,035,649 |
+| English    | en  | 3,719,484 | 608,796   |
+| German     | de  | 829,098   | 111,618   |
+| Spanish    | es  | 482,818   | 75,489    |
+| French     | fr  | 351,458   | 56,317    |
+| Italian    | it  | 155,296   | 24,562    |
+| Dutch      | nl  | 150,819   | 32,574    |
+| Portuguese | pt  | 138,778   | 26,169    |
+| Turkish    | tr  | 102,373   | 19,002    |
+| Russian    | ru  | 91,771    | 22,643    |
+| Polish     | pl  | 65,182    | 10,695    |
+| Indonesian | id  | 45,839    | 7,910     |
+| Norwegian  | no  | 37,711    | 5,143     |
+| Swedish    | sv  | 37,003    | 5,270     |
+| Danish     | da  | 32,655    | 5,279     |
+| Vietnamese | vi  | 27,157    | 5,261     |
+| Finnish    | fi  | 20,485    | 2,795     |
+| Romanian   | ro  | 17,066    | 3,554     |
+| Czech      | cs  | 16,675    | 2,568     |
+| Hebrew     | he  | 11,212    | 1,921     |
+| Hungarian  | hu  | 8,598     | 1,264     |
+| Croatian   | hr  | 5,215     | 819       |
+
+## Data Fields
+#### Nested (per page - default)
+The data is organized by page. Each page contains a list of questions and answers.
+- **id** 
+- **language**
+- **num_pairs**: the number of FAQs on the page
+- **domain**: source web domain of the FAQs
+- **qa_pairs**: a list of questions and answers
+  - **question**
+  - **answer**
+  - **language**
+  
+#### Flattened
+The data is organized by pair (i.e. pages are flattened). You can access the flat version of any language by appending `_flat` to the configuration (e.g. `en_flat`). The data will be returned pair-by-pair instead of page-by-page. 
+- **domain_id** 
+- **pair_id**
+- **language**
+- **domain**: source web domain of the FAQs
+- **question**
+- **answer**
+
+## Source Data
+
+This section was adapted from the source data description of [OSCAR](https://huggingface.co/datasets/oscar#source-data)
+
+Common Crawl is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected nofollow and robots.txt policies.
+
+To construct MFAQ, the WARC files of Common Crawl were used. We looked for `FAQPage` markup in the HTML and subsequently parsed the `FAQItem` from the page. 
+
+## People
+This model was developed by [Maxime De Bruyn](https://www.linkedin.com/in/maximedebruyn/), Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.
+
+## Licensing Information
+```
+These data are released under this licensing scheme.
+We do not own any of the text from which these data has been extracted.
+We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
+
+Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
+* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
+* Clearly identify the copyrighted work claimed to be infringed.
+* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
+
+We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
+```
+
+## Citation information
+```
+@misc{debruyn2021mfaq,
+      title={MFAQ: a Multilingual FAQ Dataset}, 
+      author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
+      year={2021},
+      eprint={2109.12870},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

huggingface_dataset/Dataset_Card/code_x_glue_cc_defect_detection.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- code
+license:
+- c-uda
+multilinguality:
+- other-programming-languages
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+pretty_name: CodeXGlueCcDefectDetection
+dataset_info:
+  features:
+  - name: id
+    dtype: int32
+  - name: func
+    dtype: string
+  - name: target
+    dtype: bool
+  - name: project
+    dtype: string
+  - name: commit_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 45723487
+    num_examples: 21854
+  - name: validation
+    num_bytes: 5582545
+    num_examples: 2732
+  - name: test
+    num_bytes: 5646752
+    num_examples: 2732
+  download_size: 61685715
+  dataset_size: 56952784
+---
+# Dataset Card for "code_x_glue_cc_defect_detection"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits-sample-size)
+- [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/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection
+
+### Dataset Summary
+
+CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection
+
+Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
+The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.
+
+### Supported Tasks and Leaderboards
+
+- `multi-class-classification`: The dataset can be used to train a model for detecting if code has a defect in it.
+
+### Languages
+
+- C **programming** language
+
+## Dataset Structure
+
+### Data Instances
+
+An example of 'validation' looks as follows.
+```
+{
+    "commit_id": "aa1530dec499f7525d2ccaa0e3a876dc8089ed1e", 
+    "func": "static void filter_mirror_setup(NetFilterState *nf, Error **errp)\n{\n    MirrorState *s = FILTER_MIRROR(nf);\n    Chardev *chr;\n    chr = qemu_chr_find(s->outdev);\n    if (chr == NULL) {\n        error_set(errp, ERROR_CLASS_DEVICE_NOT_FOUND,\n                  \"Device '%s' not found\", s->outdev);\n    qemu_chr_fe_init(&s->chr_out, chr, errp);", 
+    "id": 8, 
+    "project": "qemu", 
+    "target": true
+}
+```
+
+### Data Fields
+
+In the following each data field in go is explained for each config. The data fields are the same among all splits.
+
+#### default
+
+|field name| type |               description                |
+|----------|------|------------------------------------------|
+|id        |int32 | Index of the sample                      |
+|func      |string| The source code                          |
+|target    |bool  | 0 or 1 (vulnerability or not)            |
+|project   |string| Original project that contains this code |
+|commit_id |string| Commit identifier in the original project|
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default|21854|      2732|2732|
+
+## 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
+
+https://github.com/microsoft, https://github.com/madlag
+
+### Licensing Information
+
+Computational Use of Data Agreement (C-UDA) License.
+
+### Citation Information
+
+```
+@inproceedings{zhou2019devign,
+title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
+author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
+booktitle={Advances in Neural Information Processing Systems},
+pages={10197--10207}, year={2019}
+```
+
+### Contributions
+
+Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

huggingface_dataset/Dataset_Card/huggan_ae_photos.md

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+This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
+
+# Citation
+```
+@article{DBLP:journals/corr/ZhuPIE17,
+  author    = {Jun{-}Yan Zhu and
+               Taesung Park and
+               Phillip Isola and
+               Alexei A. Efros},
+  title     = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
+               Networks},
+  journal   = {CoRR},
+  volume    = {abs/1703.10593},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1703.10593},
+  eprinttype = {arXiv},
+  eprint    = {1703.10593},
+  timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

huggingface_dataset/Dataset_Card/imodels_credit-card.md

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+---
+annotations_creators: []
+language: []
+language_creators: []
+license: []
+multilinguality: []
+pretty_name: credit-card
+size_categories:
+- 10K<n<100K
+source_datasets: []
+tags:
+- interpretability
+- fairness
+- medicine
+task_categories:
+- tabular-classification
+task_ids: []
+---
+
+Port of the credit-card dataset from UCI (link [here](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset)). See details there and use carefully.
+
+Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb).
+
+The target is the binary outcome `default.payment.next.month`.
+
+### Sample usage
+
+Load the data:
+
+```
+from datasets import load_dataset
+
+dataset = load_dataset("imodels/credit-card")
+df = pd.DataFrame(dataset['train'])
+X = df.drop(columns=['default.payment.next.month'])
+y = df['default.payment.next.month'].values
+```
+
+Fit a model:
+
+```
+import imodels
+import numpy as np
+
+m = imodels.FIGSClassifier(max_rules=5)
+m.fit(X, y)
+print(m)
+```
+
+
+Evaluate:
+
+
+```
+df_test = pd.DataFrame(dataset['test'])
+X_test = df.drop(columns=['default.payment.next.month'])
+y_test = df['default.payment.next.month'].values
+print('accuracy', np.mean(m.predict(X_test) == y_test))
+```

huggingface_dataset/Dataset_Card/keremberke_painting-style-classification.md

@@ -0,0 +1,81 @@
+---
+task_categories:
+- image-classification
+tags:
+- roboflow
+- roboflow2huggingface
+
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/painting-style-classification" src="https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['Realism', 'Art_Nouveau_Modern', 'Analytical_Cubism', 'Cubism', 'Expressionism', 'Action_painting', 'Synthetic_Cubism', 'Symbolism', 'Ukiyo_e', 'Naive_Art_Primitivism', 'Post_Impressionism', 'Impressionism', 'Fauvism', 'Rococo', 'Minimalism', 'Mannerism_Late_Renaissance', 'Color_Field_Painting', 'High_Renaissance', 'Romanticism', 'Pop_Art', 'Contemporary_Realism', 'Baroque', 'New_Realism', 'Pointillism', 'Northern_Renaissance', 'Early_Renaissance', 'Abstract_Expressionism']
+```
+
+
+### Number of Images
+
+```json
+{'valid': 1295, 'train': 4493, 'test': 629}
+```
+
+
+### How to Use
+
+- Install [datasets](https://pypi.org/project/datasets/):
+
+```bash
+pip install datasets
+```
+
+- Load the dataset:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("keremberke/painting-style-classification", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/art-dataset/wiki-art/dataset/1](https://universe.roboflow.com/art-dataset/wiki-art/dataset/1?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ wiki-art_dataset,
+    title = { wiki art Dataset },
+    type = { Open Source Dataset },
+    author = { Art Dataset },
+    howpublished = { \\url{ https://universe.roboflow.com/art-dataset/wiki-art } },
+    url = { https://universe.roboflow.com/art-dataset/wiki-art },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { mar },
+    note = { visited on 2023-01-18 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.ai on March 9, 2022 at 1:47 AM GMT
+
+It includes 6417 images.
+27 are annotated in folder format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 416x416 (Stretch)
+
+No image augmentation techniques were applied.
+
+
+

huggingface_dataset/Dataset_Card/logannyeMD_autotrain-data-enchondroma-vs-low-grade-chondrosarcoma-histology.md

@@ -0,0 +1,53 @@
+---
+task_categories:
+- image-classification
+
+---
+# AutoTrain Dataset for project: enchondroma-vs-low-grade-chondrosarcoma-histology
+
+## Dataset Description
+
+This dataset has been automatically processed by AutoTrain for project enchondroma-vs-low-grade-chondrosarcoma-histology.
+
+### Languages
+
+The BCP-47 code for the dataset's language is unk.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from this dataset looks as follows:
+
+```json
+[
+  {
+    "image": "<1024x1024 RGB PIL image>",
+    "target": 0
+  },
+  {
+    "image": "<1024x1024 RGB PIL image>",
+    "target": 1
+  }
+]
+```
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "image": "Image(decode=True, id=None)",
+  "target": "ClassLabel(names=['Enchondroma', 'Low-grade Chondrosarcoma'], 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        | 458 |
+| valid        | 115 |

huggingface_dataset/Dataset_Card/morgan_tortas.md

@@ -0,0 +1,17 @@
+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 79653203.0
+    num_examples: 37
+  download_size: 79658169
+  dataset_size: 79653203.0
+---
+# Dataset Card for "tortas"
+
+Note that when using PyTorch's transforms that these images are 4-channel images. The last channel is all 1's and can be ignored.
+
+[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

huggingface_dataset/Dataset_Card/osyvokon_pavlick-formality-scores.md

@@ -0,0 +1,74 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- en-US
+license:
+- cc-by-3.0
+multilinguality:
+- monolingual
+pretty_name: 'Sentence-level formality annotations for news, blogs, email and QA forums.
+
+
+  Published in "An Empirical Analysis of Formality in Online Communication" (Pavlick
+  and Tetreault, 2016) '
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- text-scoring
+---
+
+
+This dataset contains sentence-level formality annotations used in the 2016
+TACL paper "An Empirical Analysis of Formality in Online Communication"
+(Pavlick and Tetreault, 2016). It includes sentences from four genres (news,
+blogs, email, and QA forums), all annotated by humans on Amazon Mechanical
+Turk. The news and blog data was collected by Shibamouli Lahiri, and we are
+redistributing it here for the convenience of other researchers. We collected
+the email and answers data ourselves, using a similar annotation setup to
+Shibamouli.
+
+In the original dataset, `answers` and `email` were tokenized. In this version,
+Oleksiy Syvokon detokenized them with `moses-detokenizer` and a bunch of
+additional regexps.
+
+If you use this data in your work, please cite BOTH of the below papers:
+
+```
+@article{PavlickAndTetreault-2016:TACL,
+  author =  {Ellie Pavlick and Joel Tetreault},
+  title =   {An Empirical Analysis of Formality in Online Communication},
+  journal = {Transactions of the Association for Computational Linguistics},
+  year =    {2016},
+  publisher = {Association for Computational Linguistics}
+}
+
+@article{Lahiri-2015:arXiv,
+  title={{SQUINKY! A} Corpus of Sentence-level Formality, Informativeness, and Implicature},
+  author={Lahiri, Shibamouli},
+  journal={arXiv preprint arXiv:1506.02306},
+  year={2015}
+}
+```
+
+## Contents
+
+The annotated data files and number of lines in each are as follows:
+
+* 4977 answers -- Annotated sentences from a random sample of posts from the Yahoo! Answers forums: https://answers.yahoo.com/
+* 1821 blog -- Annotated sentences from the top 100 blogs listed on http://technorati.com/ on October 31, 2009.
+* 1701 email -- Annotated sentences from a random sample of emails from the Jeb Bush email archive: http://americanbridgepac.org/jeb-bushs-gubernatorial-email-archive/
+* 2775 news -- Annotated sentences from the "breaking", "recent", and "local" news sections of the following 20 news sites: CNN, CBS News, ABC News, Reuters, BBC News Online, New York Times, Los Angeles Times, The Guardian (U.K.), Voice of America, Boston Globe, Chicago Tribune, San Francisco Chronicle, Times Online (U.K.), news.com.au, Xinhua, The Times of India, Seattle Post Intelligencer, Daily Mail, and Bloomberg L.P.  
+
+## Format
+
+Each record contains the following fields:
+
+1. `avg_score`: the mean formality rating, which ranges from -3 to 3 where lower scores indicate less formal sentences
+2. `sentence`

huggingface_dataset/Dataset_Card/spacemanidol_msmarco_passage_ranking.md

@@ -0,0 +1,44 @@
+# Dataset Summary
+Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
+
+
+# Dataset Creation
+
+## Source Data
+More Information Needed
+
+## Annotations
+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
+@spacemanidol
+
+# Licensing Information
+The MS MARCO datasets are intended for non-commercial research purposes only to promote advancement in the field of artificial intelligence and related areas, and is made available free of charge without extending any license or other intellectual property rights. The dataset is provided “as is” without warranty and usage of the data has risks since we may not own the underlying rights in the documents. We are not be liable for any damages related to use of the dataset. Feedback is voluntarily given and can be used as we see fit. Upon violation of any of these terms, your rights to use the dataset will end automatically.
+
+Please contact us at ms-marco@microsoft.com if you own any of the documents made available but do not want them in this dataset. We will remove the data accordingly. If you have questions about use of the dataset or any research outputs in your products or services, we encourage you to undertake your own independent legal review. For other questions, please feel free to contact us.
+
+# Citation Information
+@article{Campos2016MSMA,
+  title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
+  author={Daniel Fernando Campos and T. Nguyen and M. Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and L. Deng and Bhaskar Mitra},
+  journal={ArXiv},
+  year={2016},
+  volume={abs/1611.09268}
+}
+#Contributions
+@spacemanidol

huggingface_dataset/Dataset_Card/squad_v1_pt.md

@@ -0,0 +1,217 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- pt
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+- open-domain-qa
+paperswithcode_id: null
+pretty_name: SquadV1Pt
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: context
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answers
+    sequence:
+    - name: text
+      dtype: string
+    - name: answer_start
+      dtype: int32
+  splits:
+  - name: train
+    num_bytes: 85323237
+    num_examples: 87599
+  - name: validation
+    num_bytes: 11265474
+    num_examples: 10570
+  download_size: 39532595
+  dataset_size: 96588711
+---
+
+# Dataset Card for "squad_v1_pt"
+
+## 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/nunorc/squad-v1.1-pt](https://github.com/nunorc/squad-v1.1-pt)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 37.70 MB
+- **Size of the generated dataset:** 92.24 MB
+- **Total amount of disk used:** 129.94 MB
+
+### Dataset Summary
+
+Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### default
+
+- **Size of downloaded dataset files:** 37.70 MB
+- **Size of the generated dataset:** 92.24 MB
+- **Total amount of disk used:** 129.94 MB
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "answers": {
+        "answer_start": [0],
+        "text": ["Saint Bernadette Soubirous"]
+    },
+    "context": "\"Arquitetonicamente, a escola tem um caráter católico. No topo da cúpula de ouro do edifício principal é uma estátua de ouro da ...",
+    "id": "5733be284776f41900661182",
+    "question": "A quem a Virgem Maria supostamente apareceu em 1858 em Lourdes, na França?",
+    "title": "University_of_Notre_Dame"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `id`: a `string` feature.
+- `title`: a `string` feature.
+- `context`: a `string` feature.
+- `question`: a `string` feature.
+- `answers`: a dictionary feature containing:
+  - `text`: a `string` feature.
+  - `answer_start`: a `int32` feature.
+
+### Data Splits
+
+| name    | train | validation |
+| ------- | ----: | ---------: |
+| default | 87599 |      10570 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

huggingface_dataset/Dataset_Card/surrey-nlp_S3D-v1.md

@@ -0,0 +1,37 @@
+---
+annotations_creators:
+- Jordan Painter, Diptesh Kanojia
+language:
+- en
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: 'Utilising Weak Supervision to create S3D: A Sarcasm Annotated Dataset'
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+---
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+
+- 
+# Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset
+This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models.
+
+# S3D Summary
+The S3D dataset is our silver standard dataset of 100,000 tweets labelled for sarcasm using weak supervision by our **BERTweet-sarcasm-combined** model.
+These tweets can be accessed by using the Twitter API so that they can be used for other experiments.
+S3D contains 38879 tweets labelled as sarcastic, and 61211 tweets labelled as not being sarcastic.
+# Data Fields
+- Tweet ID: The ID of the labelled tweet
+- Label: A label to denote if a given tweet is sarcastic
+
+# Data Splits
+- Train: 70,000
+- Valid: 15,000
+- Test: 15,000

huggingface_dataset/Dataset_Card/tarekeldeeb_ArabicCorpus2B.md

@@ -0,0 +1,29 @@
+---
+license: other
+---
+```
+BUILDING VOCABULARY
+Processed 1754541204 tokens.
+Counted 5329509 unique words.
+Truncating vocabulary at min count 5.
+Using vocabulary of size 1539115.
+```
+---
+# Build the Arabic Corpus
+#### Dowload Resources
+The arabic corpus {1.9B word} consists of the following resources:
+ - ShamelaLibrary348.7z [link](https://www.quran.tv/ketab/ShamelaLibrary348.7z) {1.15B}
+ - UN arabic corpus [mirror1](http://lotus.kuee.kyoto-u.ac.jp/~raj/rajwindroot/corpora_downloads/UN_CORPUS/UNv1.0.6way.ar.txt) [mirror2](http://corpus.leeds.ac.uk/bogdan/resources/UN-corpus/6way/UNv1.0.6way.ar.txt) {0.37B}
+ - AraCorpus.tar.gz [link](http://aracorpus.e3rab.com/argistestsrv.nmsu.edu/AraCorpus.tar.gz) {0.14B}
+ - Arabic Wikipedia Latest Articles Dump [link](https://dumps.wikimedia.org/arwiki/latest/arwiki-latest-pages-articles.xml.bz2) {0.11B}
+ - Tashkeela-arabic-diacritized-text-utf8-0.3.zip [link](https://netix.dl.sourceforge.net/project/tashkeela/) {0.07B}
+ - Arabic Tweets [link](https://github.com/bakrianoo/Datasets) {0.03B}
+ - watan-2004.7z [link](https://netix.dl.sourceforge.net/project/arabiccorpus/watan-2004corpus/watan-2004.7z) {0.01B}
+#### Build Script: https://github.com/tarekeldeeb/GloVe-Arabic/tree/master/arabic_corpus
+
+---
+# Download the dataset
+Mirror : https://archive.org/details/arabic_corpus
+
+---
+license: Waqf v2 (https://github.com/ojuba-org/waqf/tree/master/2.0)