Upload batch 324 (20 files, last=huggingface_dataset/Dataset_Card/mbazaNLP_kinyarwanda-tts-dataset.md)

huggingface_dataset/Dataset_Card/Biomedical-TeMU_SPACCC_Sentence-Splitter.md

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+---
+license: cc-by-4.0
+---
+
+# The Sentence Splitter (SS) for Clinical Cases Written in Spanish
+
+## Introduction
+This repository contains the sentence splitting model trained using the SPACCC_SPLIT corpus (https://github.com/PlanTL-SANIDAD/SPACCC_SPLIT). The model was trained using the 90% of the corpus (900 clinical cases) and tested against the 10% (100 clinical cases). This model is a great resource to split sentences in biomedical documents, specially clinical cases written in Spanish. This model obtains a F-Measure of 98.75%.
+
+This model was created using the Apache OpenNLP machine learning toolkit (https://opennlp.apache.org/), with the release number 1.8.4, released in December 2017.
+
+This repository contains the model, training set, testing set, Gold Standard, executable file, and the source code.
+
+## Prerequisites
+This software has been compiled with Java SE 1.8 and it should work with recent versions. You can download Java from the following website: https://www.java.com/en/download
+
+The executable file already includes the Apache OpenNLP dependencies inside, so the download of this toolkit is not necessary. However, you may download the latest version from this website: https://opennlp.apache.org/download.html
+
+The library file we have used to compile is "opennlp-tools-1.8.4.jar". The source code should be able to compile with the latest version of OpenNLP, "opennlp-tools-*RELEASE_NUMBER*.jar". In case there are compilation or execution errors, please let us know and we will make all the necessary updates.
+
+## Directory structure
+<pre>
+exec/
+  An executable file that can be used to apply the sentence splitter to your documents.
+  You can find the notes about its execution below in section "Usage".
+
+gold_standard/
+  The clinical cases used as gold standard to evaluate the model's performance.
+
+model/
+  The sentence splitting model, "es-sentence-splitter-model-spaccc.bin", a binary file.
+
+src/
+  The source code to create the model (CreateModelSS.java) and evaluate it (EvaluateModelSS.java).
+  The directory includes an example about how to use the model inside your code (SentenceSplitter.java).
+  File "abbreviations.dat" contains a list of abbreviations, essential to build the model.
+
+test_set/
+  The clinical cases used as test set to evaluate the model's performance.
+
+train_set/
+  The clinical cases used to build the model. We use a single file with all documents present in
+  directory "train_set_docs" concatented.
+
+train_set_docs/
+  The clinical cases used to build the model. For each record the sentences are already splitted.
+
+</pre>
+
+## Usage
+The executable file *SentenceSplitter.jar* is the program you need to split the sentences of the document. For this program, two arguments are needed: (1) the text file to split the sentences, and (2) the model file (*es-sentence-splitter-model-spaccc.bin*). The program will display all sentences splitted in the terminal, with one sentence per line.
+
+From the `exec` folder, type the following command in your terminal:
+
+<pre>
+$ java -jar SentenceSplitter.jar INPUT_FILE MODEL_FILE
+</pre>
+
+## Examples
+
+Assuming you have the executable file, the input file and the model file in the same directory:
+<pre>
+$ java -jar SentenceSplitter.jar file_with_sentences_not_splitted.txt es-sentence-splitter-model-spaccc.bin
+</pre>
+
+## Model creation
+To create this sentence splitting model, we used the following training parameters (class *TrainingParameters* in OpenNLP) to get the best performance:
+- Number of iterations: 4000.
+- Cutoff parameter: 3.
+- Trainer type parameter: *EventTrainer.EVENT_VALUE*.
+- Algorithm: Maximum Entropy (*ModelType.MAXENT.name()*).
+
+Meanwhile, we used the following parameters for the sentence split builder (class *SentenceDetectorFactory* in OpenNLP) to get the best performance:
+- Subclass name: null value.
+- Language code: *es* (for Spanish).
+- Use token end: true.
+- Abbreviation dictionary: file "abbreviations.dat" (included in the `src/` directory).
+- End of file characters: ".", "?" and "!".
+
+## Model evaluation
+
+After tuning the model using different values for each parameter mentioned above, we got the best performance with the values mentioned above.
+
+|      | Value |
+| ----------------------------------------: | :------ |
+| Number of sentences in the gold standard | 1445   |
+| Number of sentences generated            | 1447   |
+| Number of sentences correctly splitted   | 1428   |
+| Number of sentences wrongly splitted     | 12     |
+| Number of sentences missed     | 5     |
+| **Precision**                                | **98.69%** |
+| **Recall**                                   | **98.82%** |
+| **F-Measure**                                | **98.75%**|
+
+Table 1: Evaluation statistics for the sentence splitting model.
+
+
+## Contact
+
+Ander Intxaurrondo (ander.intxaurrondo@bsc.es)
+
+
+## License
+
+<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
+
+Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)

huggingface_dataset/Dataset_Card/Datatang_Mandarin_Voiceprint_Recognition_Speech_Data_by_Mobile_Phone.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/Mandarin_Voiceprint_Recognition_Speech_Data_by_Mobile_Phone
+
+## 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/3HG3YZd
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Each person's time span is very long, which can better cover the sound features of a person in different periods and different states.
+                           
+For more details, please refer to the link: https://bit.ly/3HG3YZd
+
+### 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
+
+Chinese Mandarin
+## 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/NeuralInternet_awesome-chattensor-prompts.md

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+---
+license: cc0-1.0
+tags:
+- ChatGPT
+- Chattensor
+---
+<p align="center"><h1>🧠 Awesome Chaττensor Prompts [CSV dataset]</h1></p>
+
+This is a Dataset Repository of **Awesome Chattensor Prompts**
+
+**[View All Prompts on GitHub](https://github.com/neuralinternet/awesome-chattensor-prompts)**
+
+# License
+
+CC-0

huggingface_dataset/Dataset_Card/Pavithree_eli5.md

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+This dataset is the subset of original eli5 dataset available on hugging face 

huggingface_dataset/Dataset_Card/Ruohao_pcmr.md

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+---
+language:
+- en
+paperswithcode_id: coqa
+pretty_name: Conversational Question Answering Challenge
+---
+
+# Dataset Card for "coqa"
+
+## 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://stanfordnlp.github.io/coqa/](https://stanfordnlp.github.io/coqa/)
+- **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:** 55.40 MB
+- **Size of the generated dataset:** 18.35 MB
+- **Total amount of disk used:** 73.75 MB
+
+### Dataset Summary
+
+CoQA: A Conversational Question Answering Challenge
+
+### 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:** 55.40 MB
+- **Size of the generated dataset:** 18.35 MB
+- **Total amount of disk used:** 73.75 MB
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "answers": "{\"answer_end\": [179, 494, 511, 545, 879, 1127, 1128, 94, 150, 412, 1009, 1046, 643, -1, 764, 724, 125, 1384, 881, 910], \"answer_...",
+    "questions": "[\"When was the Vat formally opened?\", \"what is the library for?\", \"for what subjects?\", \"and?\", \"what was started in 2014?\", \"ho...",
+    "source": "wikipedia",
+    "story": "\"The Vatican Apostolic Library (), more commonly called the Vatican Library or simply the Vat, is the library of the Holy See, l..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `source`: a `string` feature.
+- `story`: a `string` feature.
+- `questions`: a `list` of `string` features.
+- `answers`: a dictionary feature containing:
+  - `input_text`: a `string` feature.
+  - `answer_start`: a `int32` feature.
+  - `answer_end`: a `int32` feature.
+
+### Data Splits
+
+| name  |train|validation|
+|-------|----:|---------:|
+|default| 7199|       500|
+
+## 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
+
+```
+@InProceedings{SivaAndAl:Coca,
+       author = {Siva, Reddy and Danqi, Chen and  Christopher D., Manning},
+        title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},
+      journal = { arXiv},
+         year = {2018},
+
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@ojasaar](https://github.com/ojasaar), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
+

huggingface_dataset/Dataset_Card/aeslc.md

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+---
+annotations_creators:
+- crowdsourced
+language:
+- en
+language_creators:
+- found
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: 'AESLC: Annotated Enron Subject Line Corpus'
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- summarization
+task_ids: []
+paperswithcode_id: aeslc
+tags:
+- aspect-based-summarization
+- conversations-summarization
+- multi-document-summarization
+- email-headline-generation
+dataset_info:
+  features:
+  - name: email_body
+    dtype: string
+  - name: subject_line
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 11902668
+    num_examples: 14436
+  - name: validation
+    num_bytes: 1660730
+    num_examples: 1960
+  - name: test
+    num_bytes: 1384177
+    num_examples: 1906
+  download_size: 11643743
+  dataset_size: 14947575
+---
+
+# Dataset Card for "aeslc"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:** https://github.com/ryanzhumich/AESLC
+- **Paper:** [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://arxiv.org/abs/1906.03497)
+- **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:** 11.10 MB
+- **Size of the generated dataset:** 14.26 MB
+- **Total amount of disk used:** 25.36 MB
+
+### Dataset Summary
+
+A collection of email messages of employees in the Enron Corporation.
+
+There are two features:
+  - email_body: email body text.
+  - subject_line: email subject text.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+Monolingual English (mainly en-US) with some exceptions.
+
+## Dataset Structure
+
+### Data Instances
+
+#### default
+
+- **Size of downloaded dataset files:** 11.10 MB
+- **Size of the generated dataset:** 14.26 MB
+- **Total amount of disk used:** 25.36 MB
+
+An example of 'train' looks as follows.
+```
+{
+    "email_body": "B/C\n<<some doc>>\n",
+    "subject_line": "Service Agreement"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `email_body`: a `string` feature.
+- `subject_line`: a `string` feature.
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default|14436|      1960|1906|
+
+## 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
+
+```
+@inproceedings{zhang-tetreault-2019-email,
+    title = "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation",
+    author = "Zhang, Rui  and
+      Tetreault, Joel",
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P19-1043",
+    doi = "10.18653/v1/P19-1043",
+    pages = "446--456",
+}
+```
+
+### Contributions
+
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__41-inverse-scaling__41-150015-1682059402.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- inverse-scaling/41
+eval_info:
+  task: text_zero_shot_classification
+  model: inverse-scaling/opt-66b_eval
+  metrics: []
+  dataset_name: inverse-scaling/41
+  dataset_config: inverse-scaling--41
+  dataset_split: train
+  col_mapping:
+    text: prompt
+    classes: classes
+    target: answer_index
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: inverse-scaling/opt-66b_eval
+* Dataset: inverse-scaling/41
+* Config: inverse-scaling--41
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-emotion-default-34e541-17396354.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- emotion
+eval_info:
+  task: multi_class_classification
+  model: lewiswatson/distilbert-base-uncased-finetuned-emotion
+  metrics: []
+  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: lewiswatson/distilbert-base-uncased-finetuned-emotion
+* 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/bigbio_iepa.md

@@ -0,0 +1,46 @@
+
+---
+language: 
+- en
+bigbio_language: 
+- English
+license: unknown
+multilinguality: monolingual
+bigbio_license_shortname: UNKNOWN
+pretty_name: IEPA
+homepage: http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html
+bigbio_pubmed: True
+bigbio_public: True
+bigbio_tasks: 
+- RELATION_EXTRACTION
+---
+
+
+# Dataset Card for IEPA
+
+## Dataset Description
+
+- **Homepage:** http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html
+- **Pubmed:** True
+- **Public:** True
+- **Tasks:** RE
+
+
+The IEPA benchmark PPI corpus is designed for relation extraction. It was created from 303 PubMed abstracts, each of which contains a specific pair of co-occurring chemicals.
+
+
+
+## Citation Information
+
+```
+@ARTICLE{ding2001mining,
+  title    = "Mining {MEDLINE}: abstracts, sentences, or phrases?",
+  author   = "Ding, J and Berleant, D and Nettleton, D and Wurtele, E",
+  journal  = "Pac Symp Biocomput",
+  pages    = "326--337",
+  year     =  2002,
+  address  = "United States",
+  language = "en"
+}
+
+```

huggingface_dataset/Dataset_Card/biglam_loc_beyond_words.md

@@ -0,0 +1,198 @@
+---
+dataset_info:
+  features:
+  - name: image_id
+    dtype: int64
+  - name: image
+    dtype: image
+  - name: width
+    dtype: int32
+  - name: height
+    dtype: int32
+  - name: objects
+    sequence:
+    - name: bw_id
+      dtype: string
+    - name: category_id
+      dtype:
+        class_label:
+          names:
+            '0': Photograph
+            '1': Illustration
+            '2': Map
+            '3': Comics/Cartoon
+            '4': Editorial Cartoon
+            '5': Headline
+            '6': Advertisement
+    - name: image_id
+      dtype: string
+    - name: id
+      dtype: int64
+    - name: area
+      dtype: int64
+    - name: bbox
+      sequence: float32
+      length: 4
+    - name: iscrowd
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 2854507
+    num_examples: 2846
+  - name: validation
+    num_bytes: 731782
+    num_examples: 712
+  download_size: 1200053819
+  dataset_size: 3586289
+license: cc0-1.0
+task_categories:
+- object-detection
+tags:
+- lam
+- newspapers
+- document-layout
+pretty_name: Beyond Words
+size_categories:
+- 1K<n<10K
+---
+
+# Dataset Card for Beyond Words
+
+## 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://labs.loc.gov/
+- **Repository:** https://github.com/LibraryOfCongress/newspaper-navigator
+- **Paper:** https://arxiv.org/abs/2005.01583
+- **Leaderboard:**
+- **Point of Contact:** LC-Labs@loc.gov
+
+### Dataset Summary
+
+[More Information Needed]
+
+### 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
+
+```bibtex
+@inproceedings{10.1145/3340531.3412767,
+author = {Lee, Benjamin Charles Germain and Mears, Jaime and Jakeway, Eileen and Ferriter, Meghan and Adams, Chris and Yarasavage, Nathan and Thomas, Deborah and Zwaard, Kate and Weld, Daniel S.},
+title = {The Newspaper Navigator Dataset: Extracting Headlines and Visual Content from 16 Million Historic Newspaper Pages in Chronicling America},
+year = {2020},
+isbn = {9781450368599},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3340531.3412767},
+doi = {10.1145/3340531.3412767},
+abstract = {Chronicling America is a product of the National Digital Newspaper Program, a partnership between the Library of Congress and the National Endowment for the Humanities to digitize historic American newspapers. Over 16 million pages have been digitized to date, complete with high-resolution images and machine-readable METS/ALTO OCR. Of considerable interest to Chronicling America users is a semantified corpus, complete with extracted visual content and headlines. To accomplish this, we introduce a visual content recognition model trained on bounding box annotations collected as part of the Library of Congress's Beyond Words crowdsourcing initiative and augmented with additional annotations including those of headlines and advertisements. We describe our pipeline that utilizes this deep learning model to extract 7 classes of visual content: headlines, photographs, illustrations, maps, comics, editorial cartoons, and advertisements, complete with textual content such as captions derived from the METS/ALTO OCR, as well as image embeddings. We report the results of running the pipeline on 16.3 million pages from the Chronicling America corpus and describe the resulting Newspaper Navigator dataset, the largest dataset of extracted visual content from historic newspapers ever produced. The Newspaper Navigator dataset, finetuned visual content recognition model, and all source code are placed in the public domain for unrestricted re-use.},
+booktitle = {Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management},
+pages = {3055–3062},
+numpages = {8},
+keywords = {digital humanities, dataset, chronicling america, newspaper navigator, document analysis, information retrieval, digital libraries and archives, public domain, historic newspapers},
+location = {Virtual Event, Ireland},
+series = {CIKM '20}
+}
+```
+
+### Contributions
+
+Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.

huggingface_dataset/Dataset_Card/cahya_fleurs.md

@@ -0,0 +1,310 @@
+---
+annotations_creators:
+- expert-generated
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- afr
+- amh
+- ara
+- asm
+- ast
+- azj
+- bel
+- ben
+- bos
+- cat
+- ceb
+- cmn
+- ces
+- cym
+- dan
+- deu
+- ell
+- eng
+- spa
+- est
+- fas
+- ful
+- fin
+- tgl
+- fra
+- gle
+- glg
+- guj
+- hau
+- heb
+- hin
+- hrv
+- hun
+- hye
+- ind
+- ibo
+- isl
+- ita
+- jpn
+- jav
+- kat
+- kam
+- kea
+- kaz
+- khm
+- kan
+- kor
+- ckb
+- kir
+- ltz
+- lug
+- lin
+- lao
+- lit
+- luo
+- lav
+- mri
+- mkd
+- mal
+- mon
+- mar
+- msa
+- mlt
+- mya
+- nob
+- npi
+- nld
+- nso
+- nya
+- oci
+- orm
+- ory
+- pan
+- pol
+- pus
+- por
+- ron
+- rus
+- bul
+- snd
+- slk
+- slv
+- sna
+- som
+- srp
+- swe
+- swh
+- tam
+- tel
+- tgk
+- tha
+- tur
+- ukr
+- umb
+- urd
+- uzb
+- vie
+- wol
+- xho
+- yor
+- yue
+- zul
+license:
+- cc-by-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech
+  (XTREME-S) benchmark is a benchmark designed to evaluate speech representations
+  across languages, tasks, domains and data regimes. It covers 102 languages from
+  10+ language families, 3 different domains and 4 task families: speech recognition,
+  translation, classification and retrieval.'
+tags:
+- speech-recognition
+---
+
+# FLEURS
+
+## Dataset Description
+
+- **Fine-Tuning script:** [pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition)
+- **Paper:** [FLEURS: Few-shot Learning Evaluation of
+Universal Representations of Speech](https://arxiv.org/abs/2205.12446)
+- **Total amount of disk used:** ca. 350 GB
+
+Fleurs is the speech version of the [FLoRes machine translation benchmark](https://arxiv.org/abs/2106.03193). 
+We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. 
+
+Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is
+used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: 
+
+- **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* 
+- **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian*
+- **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek*
+- **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu*
+- **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu*
+- **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese*
+- **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean*
+
+
+## Supported Tasks
+
+### 1. Speech Recognition (ASR)
+
+```py
+from datasets import load_dataset
+
+fleurs_asr = load_dataset("google/fleurs", "af_za")  # for Afrikaans
+# to download all data for multi-lingual fine-tuning uncomment following line
+# fleurs_asr = load_dataset("google/fleurs", "all")
+
+# see structure
+print(fleurs_asr)
+
+# load audio sample on the fly
+audio_input = fleurs_asr["train"][0]["audio"]  # first decoded audio sample
+transcription = fleurs_asr["train"][0]["transcription"]  # first transcription
+# use `audio_input` and `transcription` to fine-tune your model for ASR
+
+# for analyses see language groups
+all_language_groups = fleurs_asr["train"].features["lang_group_id"].names
+lang_group_id = fleurs_asr["train"][0]["lang_group_id"]
+
+all_language_groups[lang_group_id]
+```
+
+### 2. Language Identification
+
+LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all.
+
+```py
+from datasets import load_dataset
+
+fleurs_langID = load_dataset("google/fleurs", "all") # to download all data
+
+# see structure
+print(fleurs_langID)
+
+# load audio sample on the fly
+audio_input = fleurs_langID["train"][0]["audio"]  # first decoded audio sample
+language_class = fleurs_langID["train"][0]["lang_id"]  # first id class
+language = fleurs_langID["train"].features["lang_id"].names[language_class]
+
+# use audio_input and language_class to fine-tune your model for audio classification
+```
+
+### 3. Retrieval
+
+Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult.
+
+```py
+from datasets import load_dataset
+
+fleurs_retrieval = load_dataset("google/fleurs", "af_za")  # for Afrikaans
+# to download all data for multi-lingual fine-tuning uncomment following line
+# fleurs_retrieval = load_dataset("google/fleurs", "all")
+
+# see structure
+print(fleurs_retrieval)
+
+# load audio sample on the fly
+audio_input = fleurs_retrieval["train"][0]["audio"]  # decoded audio sample
+text_sample_pos = fleurs_retrieval["train"][0]["transcription"]  # positive text sample
+text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples
+
+# use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval
+```
+
+Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech.
+
+## Dataset Structure
+
+We show detailed information the example configurations `af_za` of the dataset.
+All other configurations have the same structure.
+
+### Data Instances
+
+**af_za**
+- Size of downloaded dataset files: 1.47 GB
+- Size of the generated dataset: 1 MB
+- Total amount of disk used: 1.47 GB
+
+An example of a data instance of the config `af_za` looks as follows:
+
+```
+{'id': 91,
+ 'num_samples': 385920,
+ 'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav',
+ 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav',
+  'array': array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
+         -1.1205673e-04, -8.4638596e-05, -1.2731552e-04], dtype=float32),
+  'sampling_rate': 16000},
+ 'raw_transcription': 'Dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin',
+ 'transcription': 'dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin',
+ 'gender': 0,
+ 'lang_id': 0,
+ 'language': 'Afrikaans',
+ 'lang_group_id': 3}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+- **id** (int): ID of audio sample
+- **num_samples** (int): Number of float values
+- **path** (str): Path to the audio file
+- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
+- **raw_transcription** (str): The non-normalized transcription of the audio file
+- **transcription** (str): Transcription of the audio file
+- **gender** (int): Class id of gender
+- **lang_id** (int): Class id of language
+- **lang_group_id** (int): Class id of language group
+
+### Data Splits
+
+Every config only has the `"train"` split containing of *ca.* 1000 examples, and a `"validation"` and `"test"` split each containing of *ca.* 400 examples.
+
+## Dataset Creation
+
+We collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for
+train, dev and test respectively.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos).
+
+### Discussion of Biases
+
+Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages.
+
+### Other Known Limitations
+
+The dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding.
+
+## Additional Information
+
+All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
+
+### Citation Information
+
+You can access the FLEURS paper at https://arxiv.org/abs/2205.12446.
+Please cite the paper when referencing the FLEURS corpus as:
+
+```
+@article{fleurs2022arxiv,
+  title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
+  author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
+  journal={arXiv preprint arXiv:2205.12446},
+  url = {https://arxiv.org/abs/2205.12446},
+  year = {2022},
+```
+
+### Contributions
+
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@aconneau](https://github.com/aconneau) for adding this dataset.

huggingface_dataset/Dataset_Card/huggingnft_dooggies.md

@@ -0,0 +1,175 @@
+---
+tags:
+- huggingnft
+- nft
+- huggan
+- gan
+- image
+- images
+task: 
+- unconditional-image-generation
+datasets:
+- huggingnft/dooggies
+license: mit
+---
+
+# Dataset Card
+
+## Disclaimer
+
+All rights belong to their owners.
+Models and datasets can be removed from the site at the request of the copyright holder.
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [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)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
+- **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)
+
+
+### Dataset Summary
+
+NFT images dataset for unconditional generation.
+
+NFT collection available [here](https://opensea.io/collection/dooggies).
+
+Model is available [here](https://huggingface.co/huggingnft/dooggies).
+
+Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingnft/dooggies")
+```
+
+## Dataset Structure
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `image`: an `image` feature.
+- `id`: an `int` feature.
+- `token_metadata`: a `str` feature.
+- `image_original_url`: a `str` feature.
+
+### Data Splits
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+
+## 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
+
+```
+@InProceedings{huggingnft,
+    author={Aleksey Korshuk}
+    year=2022
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft)

huggingface_dataset/Dataset_Card/irds_mmarco_fr_dev.md

@@ -0,0 +1,49 @@
+---
+pretty_name: '`mmarco/fr/dev`'
+viewer: false
+source_datasets: ['irds/mmarco_fr']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mmarco/fr/dev`
+
+The `mmarco/fr/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/fr/dev).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=101,093
+ - `qrels`: (relevance assessments); count=59,273
+
+ - For `docs`, use [`irds/mmarco_fr`](https://huggingface.co/datasets/irds/mmarco_fr)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/mmarco_fr_dev', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mmarco_fr_dev', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@article{Bonifacio2021MMarco,
+    title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
+    author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
+    year={2021},
+    journal={arXiv:2108.13897}
+}
+```

huggingface_dataset/Dataset_Card/irds_mr-tydi_ja_dev.md

@@ -0,0 +1,55 @@
+---
+pretty_name: '`mr-tydi/ja/dev`'
+viewer: false
+source_datasets: ['irds/mr-tydi_ja']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mr-tydi/ja/dev`
+
+The `mr-tydi/ja/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/ja/dev).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=928
+ - `qrels`: (relevance assessments); count=928
+
+ - For `docs`, use [`irds/mr-tydi_ja`](https://huggingface.co/datasets/irds/mr-tydi_ja)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/mr-tydi_ja_dev', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mr-tydi_ja_dev', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@article{Zhang2021MrTyDi,
+  title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, 
+  author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
+  year={2021},
+  journal={arXiv:2108.08787},
+}
+@article{Clark2020TyDiQa,
+    title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
+    author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
+    year={2020},
+    journal={Transactions of the Association for Computational Linguistics}
+}
+```

huggingface_dataset/Dataset_Card/irds_wikiclir_zh.md

@@ -0,0 +1,63 @@
+---
+pretty_name: '`wikiclir/zh`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `wikiclir/zh`
+
+The `wikiclir/zh` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/zh).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=951,480
+ - `queries` (i.e., topics); count=463,273
+ - `qrels`: (relevance assessments); count=926,130
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/wikiclir_zh', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'title': ..., 'text': ...}
+
+queries = load_dataset('irds/wikiclir_zh', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/wikiclir_zh', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@inproceedings{sasaki-etal-2018-cross,
+    title = "Cross-Lingual Learning-to-Rank with Shared Representations",
+    author = "Sasaki, Shota  and
+      Sun, Shuo  and
+      Schamoni, Shigehiko  and
+      Duh, Kevin  and
+      Inui, Kentaro",
+    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
+    month = jun,
+    year = "2018",
+    address = "New Orleans, Louisiana",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N18-2073",
+    doi = "10.18653/v1/N18-2073",
+    pages = "458--463"
+}
+```

huggingface_dataset/Dataset_Card/koutch_refactory_program_repair.md

@@ -0,0 +1,97 @@
+---
+dataset_info:
+  features:
+  - name: submission_id
+    dtype: int64
+  - name: assignment_id
+    dtype: int64
+  - name: func_code
+    dtype: string
+  - name: func_name
+    dtype: string
+  - name: test
+    dtype: string
+  - name: description
+    dtype: string
+  - name: annotation
+    dtype: string
+  - name: comments
+    dtype: string
+  - name: __index_level_0__
+    dtype: float64
+  splits:
+  - name: train
+    num_bytes: 962
+    num_examples: 1
+  download_size: 0
+  dataset_size: 962
+---
+# Dataset Card for "refactory_program_repair"
+
+## Dataset Description
+
+- **Paper: coming soon** 
+
+### Dataset Summary
+
+The "refactory_program_repair" dataset is a subset of the [refactory_programming_data](https://huggingface.co/datasets/koutch/refactory_programming_data) 
+augmented with educators' annotations on the corrections to the buggy programs and comments on the reason for the solution not working.
+
+### Supported Tasks and Leaderboards
+
+This dataset can be used for the task of program repair. In [Computing Education Research (CER)](https://faculty.washington.edu/ajko/cer/), 
+methods for automatically repairing student programs are used to provide students with feedback and help them debug their code. 
+
+### Languages
+
+The assignments were written in Python - English 
+
+## Dataset Structure
+
+### Data Fields
+
+* submission_id: a unique number identifying the submission
+* func_code: the cleaned code submitted
+* func_name: the name of the function that had to be implemented
+* assingment_id: the unique (string) identifier of the assignment that had to be completed
+* description: the assignment description
+* test: a humaneval style string which can be used to execute the submitted solution on the provided test cases
+* annotation: the annotators correction to buggy program
+* comments: annotators' short comment on why the type of bug in the solution
+
+
+## Dataset Creation
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[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]
+
+### Citation Information
+
+[Coming soon]

huggingface_dataset/Dataset_Card/lmqg_qag_tweetqa.md

@@ -0,0 +1,71 @@
+---
+license: cc-by-sa-4.0
+pretty_name: TweetQA for question generation
+language: en
+multilinguality: monolingual
+size_categories: 1k<n<10K
+source_datasets: tweet_qa
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+tags:
+- question-generation
+---
+
+# Dataset Card for "lmqg/qag_tweetqa"
+
+
+## Dataset Description
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
+- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
+
+### Dataset Summary
+This is the question & answer generation dataset based on the [tweet_qa](https://huggingface.co/datasets/tweet_qa). The test set of the original data is not publicly released, so we randomly sampled test questions from the training set.
+
+### Supported Tasks and Leaderboards
+* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
+  Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
+
+### Languages
+English (en)
+
+## Dataset Structure
+An example of 'train' looks as follows.
+```
+{
+  "paragraph": "I would hope that Phylicia Rashad would apologize now that @missjillscott has! You cannot discount 30 victims who come with similar stories.— JDWhitner (@JDWhitner) July 7, 2015",
+  "questions": [ "what should phylicia rashad do now?", "how many victims have come forward?" ],
+  "answers": [ "apologize", "30" ],
+  "questions_answers": "Q: what should phylicia rashad do now?, A: apologize Q: how many victims have come forward?, A: 30"
+}
+```
+The data fields are the same among all splits.
+- `questions`: a `list` of `string` features. 
+- `answers`: a `list` of `string` features.
+- `paragraph`: a `string` feature.
+- `questions_answers`: a `string` feature.
+
+## Data Splits
+
+|train|validation|test |
+|----:|---------:|----:|
+|4536 |       583|  583|
+
+
+## Citation Information
+
+```
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando  and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+```

huggingface_dataset/Dataset_Card/mbazaNLP_kinyarwanda-tts-dataset.md

@@ -0,0 +1,29 @@
+---
+language: 
+  - rw
+language_creators: 
+  - "Digital Umuganda"
+license: 
+  - cc-by-4.0
+size_categories: 
+  - 3K<n<4K
+  - ~6hours
+---
+# Kinyarwanda TTS dataset
+
+The dataset consists of 3992 clips of Kinyarwanda TTS corpus recorded in a studio using a voice actress, it was collected in the mbaza project
+
+## Data structure
+```
+Audio: 3992 Single voice studio recordings by a voice actress
+Text: CSV with audio name and corresponding written text
+```
+
+## Language
+The corresponding dataset is in the Kinyarwanda Language
+
+## Dataset Creation
+- Text collected had to include Kinyarwanda syllabes, which is made by a combination of a consonant or a group of consonats (e.g. Nyw) and a vowel.
+- Text were reviewed by a linguist to ensure the text fit kinyarwanda standards
+- The voice were recorded in a studio albeit in a semi-professional settings (i.e. some of the audio contains reverbs)
+

huggingface_dataset/Dataset_Card/softcatala_Softcatala-Web-Texts-Dataset.md

@@ -0,0 +1,105 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- expert-generated
+language:
+- 'ca'
+license:
+- cc-by-sa-4.0
+- cc0-1.0
+multilinguality:
+- monolingual
+pretty_name: Softcatalà website content.
+size_categories:
+- "100K<n<1M"
+source_datasets:
+- original
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+---
+# Dataset Card for Tilde-MODEL-Catalan
+## 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.softcatala.org/
+- **Repository:** https://github.com/Softcatala/softcatala-web-dataset
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+### Dataset Summary
+This repository contains Sofcatalà web site content (articles and programs descriptions).
+
+Dataset size:
+* articles.json contains 623 articles with 373233 words.
+* programes.json contains 330 program descriptions with 49868 words.
+
+The license of the data is Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
+### Supported Tasks and Leaderboards
+
+### Languages
+Catalan (`ca`).
+## Dataset Structure
+### Data Instances
+[More Information Needed]
+### Data Fields
+JSON (name/value pairs) format with the following fields: content, date, id and title.
+### Data Splits
+One file for the descriptions text and one for the articles text.
+## Dataset Creation
+### Curation Rationale
+[More Information Needed]
+### Source Data
+#### Initial Data Collection and Normalization
+[More Information Needed]
+#### Who are the source language producers?
+Softcatalà community.
+### 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
+[@softcatala](https://github.com/Softcatala)
+[@jordimas](https://github.com/jordimas)
+### Licensing Information
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
+[CC0-1.0](https://creativecommons.org/share-your-work/public-domain/cc0/).
+### Citation Information
+[More Information Needed]
+### Contributions
+[More Information Needed]

huggingface_dataset/Dataset_Card/tti-bias_identities.md

@@ -0,0 +1,142 @@
+---
+license: cc-by-sa-4.0
+dataset_info:
+  features:
+  - name: ethnicity
+    dtype: string
+  - name: gender
+    dtype: string
+  - name: 'no'
+    dtype: int32
+  - name: image_path
+    dtype: string
+  - name: image
+    dtype: image
+  - name: model
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 585336673.0
+    num_examples: 2040
+  download_size: 465986042
+  dataset_size: 585336673.0
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### 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]