Upload batch 138 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-ccdv__arxiv-summarization-document-47d12e-1465753970.md)

huggingface_dataset/Dataset_Card/4eJIoBek_Zelenograd-aerial-videos.md

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+---
+license: wtfpl
+---
+(almost) all aerial videos of Zelenograd until 2023. i dont have a rights of these videos, all of these were downloaded from youtube. if you an owner of some of these videos and dont want that it were there, please contact me 4eJIoBek2021@gmail.com

huggingface_dataset/Dataset_Card/Langame_langame-seeker.md

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+---
+license: wtfpl
+---
+
+# langame-seeker
+
+Self chat between two [Seeker Search-Augmented Language Model](https://parl.ai/projects/seeker/) using [Langame](https://langa.me/) conversation starters generated by Langame's proprietary language model. The 3000 conversation starters have been generated beforehand into an "offline" dataset and manually corrected and adjusted by psychologically and philosophically trained humans.
+
+The search engine source code is unfortunately private yet, some work need to be done to make it open source.

huggingface_dataset/Dataset_Card/MicPie_unpredictable_cluster-noise.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+pretty_name: UnpredicTable-cluster-noise
+size_categories:
+- 100K<n<1M
+source_datasets: []
+task_categories:
+- multiple-choice
+- question-answering
+- zero-shot-classification
+- text2text-generation
+- table-question-answering
+- text-generation
+- text-classification
+- tabular-classification
+task_ids:
+- multiple-choice-qa
+- extractive-qa
+- open-domain-qa
+- closed-domain-qa
+- closed-book-qa
+- open-book-qa
+- language-modeling
+- multi-class-classification
+- natural-language-inference
+- topic-classification
+- multi-label-classification
+- tabular-multi-class-classification
+- tabular-multi-label-classification
+---
+
+
+# Dataset Card for "UnpredicTable-cluster-noise" - Dataset of Few-shot Tasks from Tables
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://ethanperez.net/unpredictable
+- **Repository:** https://github.com/JunShern/few-shot-adaptation
+- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
+- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
+
+### Dataset Summary
+
+The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
+
+There are several dataset versions available:
+
+* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
+
+* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
+
+* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
+
+* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
+    * [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
+    * [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
+    * [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
+
+* UnpredicTable data subsets based on the website of origin:
+    * [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com) 
+    * [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
+    * [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com) 
+    * [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
+    * [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com) 
+    * [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
+    * [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
+    * [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
+    * [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
+    * [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
+    * [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
+    * [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
+    * [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
+    * [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
+    * [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
+    * [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
+    * [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
+    * [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
+    * [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
+    * [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
+
+
+* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
+    * [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
+    * [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
+    * [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
+    * [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
+    * [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
+    * [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
+    * [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
+    * [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
+    * [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
+    * [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
+    * [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
+    * [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
+    * [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
+    * [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
+    * [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
+    * [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
+    * [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
+    * [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
+    * [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
+    * [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
+    * [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
+    * [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
+    * [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
+    * [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
+    * [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
+    * [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
+    * [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
+    * [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
+    * [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
+    * [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
+    * [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
+
+### Supported Tasks and Leaderboards
+
+Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
+
+The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
+
+There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'. 
+
+### Data Fields
+
+'task': task identifier
+
+'input': column elements of a specific row in the table. 
+
+'options': for multiple choice classification, it provides the options to choose from.
+
+'output': target column element of the same row as input.
+
+'pageTitle': the title of the page containing the table. 
+
+'outputColName': output column name
+
+'url': url to the website containing the table
+
+'wdcFile': WDC Web Table Corpus file
+
+### Data Splits
+
+The UnpredicTable datasets do not come with additional data splits.
+
+## Dataset Creation
+
+### Curation Rationale
+
+Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
+
+#### Who are the source language producers?
+
+The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
+
+### Annotations
+
+#### Annotation process
+
+Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
+[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
+
+#### Who are the annotators?
+
+Annotations were carried out by a lab assistant.
+
+### Personal and Sensitive Information
+
+The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
+
+### Discussion of Biases
+
+Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
+
+### Other Known Limitations
+
+No additional known limitations.
+
+## Additional Information
+
+### Dataset Curators
+Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
+
+### Licensing Information
+Apache 2.0
+
+### Citation Information
+
+```
+@misc{chan2022few,
+  author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
+  title = {Few-shot Adaptation Works with UnpredicTable Data},
+  publisher={arXiv},
+  year = {2022},
+  url = {https://arxiv.org/abs/2208.01009}
+}
+```

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-ccdv__arxiv-summarization-document-47d12e-1465753970.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- ccdv/arxiv-summarization
+eval_info:
+  task: summarization
+  model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
+  metrics: []
+  dataset_name: ccdv/arxiv-summarization
+  dataset_config: document
+  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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
+* Dataset: ccdv/arxiv-summarization
+* Config: document
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__exampletx-constructive-7f6ba0-1708559815.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- phpthinh/exampletx
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-3b
+  metrics: []
+  dataset_name: phpthinh/exampletx
+  dataset_config: constructive
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# 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: bigscience/bloom-3b
+* Dataset: phpthinh/exampletx
+* Config: constructive
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-48057538-ec1b-4e18-ac2b-35070fb8202e-3735.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- glue
+eval_info:
+  task: binary_classification
+  model: autoevaluate/binary-classification
+  metrics: ['matthews_correlation']
+  dataset_name: glue
+  dataset_config: sst2
+  dataset_split: validation
+  col_mapping:
+    text: sentence
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Binary Text Classification
+* Model: autoevaluate/binary-classification
+* Dataset: glue
+* Config: sst2
+* 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-6fbfec76-7855038.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- samsum
+eval_info:
+  task: summarization
+  model: santiviquez/t5-small-finetuned-samsum-en
+  metrics: []
+  dataset_name: samsum
+  dataset_config: samsum
+  dataset_split: test
+  col_mapping:
+    text: dialogue
+    target: summary
+---
+# 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: santiviquez/t5-small-finetuned-samsum-en
+* Dataset: samsum
+
+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-d42d3c12-7815012.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- xtreme
+eval_info:
+  task: entity_extraction
+  model: edwardjross/xlm-roberta-base-finetuned-panx-de
+  metrics: []
+  dataset_name: xtreme
+  dataset_config: PAN-X.de
+  dataset_split: test
+  col_mapping:
+    tokens: tokens
+    tags: ner_tags
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: edwardjross/xlm-roberta-base-finetuned-panx-de
+* Dataset: xtreme
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-sst2-ee5c821a-11545531.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- sst2
+eval_info:
+  task: multi_class_classification
+  model: distilbert-base-uncased-finetuned-sst-2-english
+  metrics: []
+  dataset_name: sst2
+  dataset_config: default
+  dataset_split: train
+  col_mapping:
+    text: sentence
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Multi-class Text Classification
+* Model: distilbert-base-uncased-finetuned-sst-2-english
+* Dataset: sst2
+* Config: default
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@Neez](https://huggingface.co/Neez) for evaluating this model.

huggingface_dataset/Dataset_Card/bando168_Himnusz.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+{}
+---
+
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]

huggingface_dataset/Dataset_Card/bstds_indo_law.md

@@ -0,0 +1,21 @@
+---
+language:
+- id
+---
+
+indo-law: Indonesian law dataset containing section annotation of court decision documents
+
+https://github.com/ir-nlp-csui/indo-law
+
+```
+@article{nuranti2022predicting,
+  title={Predicting the Category and the Length of Punishment in Indonesian Courts Based on Previous Court Decision Documents},
+  author={Nuranti, Eka Qadri and Yulianti, Evi and Husin, Husna Sarirah},
+  journal={Computers},
+  volume={11},
+  number={6},
+  pages={88},
+  year={2022},
+  publisher={Multidisciplinary Digital Publishing Institute}
+}
+```

huggingface_dataset/Dataset_Card/clips_VaccinChatNL.md

@@ -0,0 +1,164 @@
+---
+annotations_creators:
+- expert-generated
+language:
+- nl
+language_creators:
+- other
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: VaccinChatNL
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- covid-19
+- FAQ
+- question-answer pairs
+task_categories:
+- text-classification
+task_ids:
+- intent-classification
+---
+
+# Dataset Card for VaccinChatNL
+
+## 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:** [To be added]
+- **Leaderboard:** -->
+- **Point of Contact:**  [Jeska Buhmann](mailto:jeska.buhmann@uantwerpen.be)
+
+### Dataset Summary
+
+VaccinChatNL is a Flemish Dutch FAQ dataset on the topic of COVID-19 vaccinations in Flanders. It consists of 12,833 user questions divided over 181 answer labels, thus providing large groups of semantically equivalent paraphrases (a many-to-one mapping of user questions to answer labels). VaccinChatNL is the first Dutch many-to-one FAQ dataset of this size.
+
+### Supported Tasks and Leaderboards
+
+- 'text-classification': the dataset can be used to train a classification model for Dutch frequently asked questions on the topic of COVID-19 vaccination in Flanders.  
+
+
+### Languages
+
+Dutch (Flemish): the BCP-47 code for Dutch as generally spoken in Flanders (Belgium) is nl-BE.
+
+## Dataset Structure
+
+### Data Instances
+
+For each instance, there is a string for the user question and a string for the label of the annotated answer. See the [CLiPS / VaccinChatNL dataset viewer](https://huggingface.co/datasets/clips/VaccinChatNL/viewer/clips--VaccinChatNL/train).
+
+```
+{"sentence1": "Waar kan ik de bijsluiters van de vaccins vinden?", "label": "faq_ask_bijsluiter"}
+```
+
+### Data Fields
+
+- `sentence1`: a string containing the user question
+- `label`: a string containing the name of the intent (the answer class)
+
+
+### Data Splits
+
+The VaccinChatNL dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics for the dataset.
+
+| Dataset Split | Number of Labeled User Questions in Split               |
+| ------------- | ------------------------------------------ |
+| Train         | 10,542                                     |
+| Validation    |  1,171                                     |
+| Test          |  1,170                                     |
+
+
+## Dataset Creation
+
+<!-- ### Curation Rationale
+
+[More Information Needed] -->
+
+<!-- ### Source Data
+
+[Perhaps a link to vaccinchat.be and some of the website that were used for information] -->
+
+<!-- #### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed] -->
+
+### Annotations
+
+#### Annotation process
+
+Annotation was an iterative semi-automatic process. Starting from a very limited dataset with approximately 50 question-answer pairs (_sentence1-label_ pairs) a text classification model was trained and implemented in a publicly available chatbot. When the chatbot was used, the predicted labels for the new questions were checked and corrected if necessary. In addition, new answers were added to the dataset. After each round of corrections, the model was retrained on the updated dataset. This iterative approach led to the final dataset containing 12,883 user questions divided over 181 answer labels.
+
+#### Who are the annotators?
+
+The VaccinChatNL data were annotated by members and students of [CLiPS](https://www.uantwerpen.be/en/research-groups/clips/). All annotators have a background in Computational Linguistics.
+
+### Personal and Sensitive Information
+
+The data are anonymized in the sense that a user question can never be traced back to a specific individual.
+
+## Considerations for Using the Data
+
+<!-- ### Social Impact of Dataset
+
+[More Information Needed] -->
+
+### Discussion of Biases
+
+This dataset contains real user questions, including a rather large section (7%) of out-of-domain questions or remarks (_label: nlu_fallback_). This class of user questions consists of ununderstandable questions, but also jokes and insulting remarks.
+
+<!-- ### Other Known Limitations
+
+[Perhaps some information of % of exact overlap between train and test set] -->
+
+## Additional Information
+
+<!-- ### Dataset Curators
+
+[More Information Needed] -->
+
+<!-- ### Licensing Information
+
+[More Information Needed] -->
+
+### Citation Information
+
+Will be added asap.
+
+<!-- ### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. -->

huggingface_dataset/Dataset_Card/faruk_bengali-names-vs-gender.md

@@ -0,0 +1,43 @@
+---
+license: afl-3.0
+---
+
+# Bengali Female VS Male Names Dataset
+An NLP dataset that contains 2030 data samples of Bengali names and corresponding gender both for female and male. This is a very small and simple toy dataset that can be used by NLP starters to practice sequence classification problem and other NLP problems like gender recognition from names.
+
+# Background
+In Bengali language, name of a person is dependent largely on their gender. Normally, name of a female ends with certain type of suffix "A", "I", "EE" ["আ", "ই", "ঈ"]. And the names of male are significantly different from female in terms of phoneme patterns and ending suffix. So, In my observation there is a significant possibility that these difference in patterns can be used for gender classification based on names.
+
+
+Find the full documentation here:
+[Documentation and dataset specifications](https://github.com/faruk-ahmad/bengali-female-vs-male-names)
+
+## Dataset Format
+The dataset is in CSV format. There are two columns- namely 
+1. Name
+2. Gender
+
+Each row has two attributes. First one is name, second one is the gender. The name attribute is in ```utf-8``` encoding. And the second attribute i.e. the gender attribute has been signified by 0 and 1 as 
+
+|   |   |
+|---|---|
+|male| 0|
+|female| 1|
+|   |   |
+
+## Dataset Statistics
+The number of samples per class is as bellow-
+
+|   |   |
+|---|---|
+|male| 1029|
+|female| 1001|
+|   |   |
+
+## Possible Use Cases
+1. Sequence Classification using RNN, LSTM etc
+2. Sequence modeling using other type of machine learning algorithms
+3. Gender recognition based on names
+
+## Disclaimer
+The names were collected from internet using different sources like wikipedia, baby name suggestion websites, blogs etc. If someones name is in the dataset, that is totally unintentional.

huggingface_dataset/Dataset_Card/frankier_cross_domain_reviews.md

@@ -0,0 +1,33 @@
+---
+language:
+- en
+language_creators:
+- found
+license: unknown
+multilinguality:
+- monolingual
+pretty_name: Blue
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|app_reviews
+tags:
+- reviews
+- ratings
+- ordinal
+- text
+task_categories:
+- text-classification
+task_ids:
+- text-scoring
+- sentiment-scoring
+---
+
+This dataset is a quick-and-dirty benchmark for predicting ratings across
+different domains and on different rating scales based on text. It pulls in a
+bunch of rating datasets, takes at most 1000 instances from each and combines
+them into a big dataset.
+
+Requires the `kaggle` library to be installed, and kaggle API keys passed
+through environment variables or in ~/.kaggle/kaggle.json. See [the Kaggle
+docs](https://www.kaggle.com/docs/api#authentication).

huggingface_dataset/Dataset_Card/huggingartists_andre-3000.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/andre-3000"
+
+## 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/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **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 the generated dataset:** 0.907585 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/64b15c9489c65f5bf8f6577334347404.434x434x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/andre-3000">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">André 3000</div>
+    <a href="https://genius.com/artists/andre-3000">
+    	<div style="text-align: center; font-size: 14px;">@andre-3000</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/andre-3000).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/andre-3000")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|338|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/andre-3000")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## 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{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## 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/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/huggingartists_pyrokinesis.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/pyrokinesis"
+
+## 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/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **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 the generated dataset:** 0.7954 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/e701c222dfb8725065dd99c8a43988da.1000x1000x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/pyrokinesis">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">​​pyrokinesis</div>
+    <a href="https://genius.com/artists/pyrokinesis">
+    	<div style="text-align: center; font-size: 14px;">@pyrokinesis</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/pyrokinesis).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/pyrokinesis")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|202|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/pyrokinesis")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## 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{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## 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/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/irds_mr-tydi_te.md

@@ -0,0 +1,62 @@
+---
+pretty_name: '`mr-tydi/te`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mr-tydi/te`
+
+The `mr-tydi/te` 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/te).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=548,224
+ - `queries` (i.e., topics); count=5,517
+ - `qrels`: (relevance assessments); count=5,540
+
+
+This dataset is used by: [`mr-tydi_te_dev`](https://huggingface.co/datasets/irds/mr-tydi_te_dev), [`mr-tydi_te_test`](https://huggingface.co/datasets/irds/mr-tydi_te_test), [`mr-tydi_te_train`](https://huggingface.co/datasets/irds/mr-tydi_te_train)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/mr-tydi_te', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ...}
+
+queries = load_dataset('irds/mr-tydi_te', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mr-tydi_te', '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/lmqg_qg_annotation.md

@@ -0,0 +1,59 @@
+---
+license: cc-by-4.0
+pretty_name: QG Annotation
+language: en
+multilinguality: monolingual
+size_categories: <1K
+---
+
+# Dataset Card for "lmqg/qg_annotation"
+
+## 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 annotated questions generated by different models, used to measure the correlation of automatic metrics against 
+human in ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
+
+### Languages
+English (en)
+
+## Dataset Structure
+An example of 'train' looks as follows.
+
+```python
+{
+    "correctness": 1.8,
+    "grammaticality": 3.0,
+    "understandability": 2.4,
+    "prediction": "What trade did the Ming dynasty have a shortage of?",
+    "Bleu_4": 0.4961682999359617,
+    "METEOR": 0.3572683356086923,
+    "ROUGE_L": 0.7272727272727273,
+    "BERTScore": 0.9142221808433532,
+    "MoverScore": 0.6782580808848975,
+    "reference_raw": "What important trade did the Ming Dynasty have with Tibet?",
+    "answer_raw": "horse trade",
+    "paragraph_raw": "Some scholars note that Tibetan leaders during the Ming frequently engaged in civil war and conducted their own foreign diplomacy with neighboring states such as Nepal. Some scholars underscore the commercial aspect of the Ming-Tibetan relationship, noting the Ming dynasty's shortage of horses for warfare and thus the importance of the horse trade with Tibet. Others argue that the significant religious nature of the relationship of the Ming court with Tibetan lamas is underrepresented in modern scholarship. In hopes of reviving the unique relationship of the earlier Mongol leader Kublai Khan (r. 1260\u20131294) and his spiritual superior Drog\u00f6n Ch\u00f6gyal Phagpa (1235\u20131280) of the Sakya school of Tibetan Buddhism, the Yongle Emperor (r. 1402\u20131424) made a concerted effort to build a secular and religious alliance with Deshin Shekpa (1384\u20131415), the Karmapa of the Karma Kagyu school. However, the Yongle Emperor's attempts were unsuccessful.",
+    "sentence_raw": "Some scholars underscore the commercial aspect of the Ming-Tibetan relationship, noting the Ming dynasty's shortage of horses for warfare and thus the importance of the horse trade with Tibet.",
+    "reference_norm": "what important trade did the ming dynasty have with tibet ?",
+    "model": "T5 Large"
+}
+```
+
+## 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/opus_books.md

@@ -0,0 +1,1156 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- ca
+- de
+- el
+- en
+- eo
+- es
+- fi
+- fr
+- hu
+- it
+- nl
+- 'no'
+- pl
+- pt
+- ru
+- sv
+license:
+- unknown
+multilinguality:
+- multilingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- translation
+task_ids: []
+paperswithcode_id: null
+pretty_name: OpusBooks
+dataset_info:
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+        - 'no'
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+        - pl
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+        - fi
+        - pl
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+        - hu
+        - pl
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+        - pt
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+    num_examples: 1102
+  download_size: 116319
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+        - en
+        - pt
+  splits:
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+- config_name: eo-pt
+  features:
+  - name: id
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+  - name: translation
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+      translation:
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+        - eo
+        - pt
+  splits:
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+    num_examples: 1259
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+  - name: translation
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+        - es
+        - pt
+  splits:
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+        - fr
+        - pt
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+- config_name: hu-pt
+  features:
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+    dtype: string
+  - name: translation
+    dtype:
+      translation:
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+        - hu
+        - pt
+  splits:
+  - name: train
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+  - name: translation
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+        - it
+        - pt
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+        - ru
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+        - en
+        - ru
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+- config_name: es-ru
+  features:
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+  - name: translation
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+        - es
+        - ru
+  splits:
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+- config_name: fr-ru
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - fr
+        - ru
+  splits:
+  - name: train
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+- config_name: hu-ru
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - hu
+        - ru
+  splits:
+  - name: train
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+    num_examples: 26127
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+- config_name: it-ru
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - it
+        - ru
+  splits:
+  - name: train
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+    num_examples: 17906
+  download_size: 1620478
+  dataset_size: 5316952
+- config_name: en-sv
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - sv
+  splits:
+  - name: train
+    num_bytes: 790785
+    num_examples: 3095
+  download_size: 304975
+  dataset_size: 790785
+- config_name: fr-sv
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - fr
+        - sv
+  splits:
+  - name: train
+    num_bytes: 833553
+    num_examples: 3002
+  download_size: 321660
+  dataset_size: 833553
+- config_name: it-sv
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - it
+        - sv
+  splits:
+  - name: train
+    num_bytes: 811413
+    num_examples: 2998
+  download_size: 307821
+  dataset_size: 811413
+---
+
+# Dataset Card for OpusBooks
+
+## 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:** http://opus.nlpl.eu/Books.php
+- **Repository:** None
+- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
+- **Leaderboard:** [More Information Needed]
+- **Point of Contact:** [More Information Needed]
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+Here are some examples of questions and facts:
+
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

huggingface_dataset/Dataset_Card/quarel.md

@@ -0,0 +1,200 @@
+---
+language:
+- en
+paperswithcode_id: quarel
+pretty_name: QuaRel
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: answer_index
+    dtype: int32
+  - name: logical_forms
+    sequence: string
+  - name: logical_form_pretty
+    dtype: string
+  - name: world_literals
+    sequence:
+    - name: world1
+      dtype: string
+    - name: world2
+      dtype: string
+  - name: question
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1072874
+    num_examples: 1941
+  - name: test
+    num_bytes: 307588
+    num_examples: 552
+  - name: validation
+    num_bytes: 154308
+    num_examples: 278
+  download_size: 631370
+  dataset_size: 1534770
+---
+
+# Dataset Card for "quarel"
+
+## 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://allenai.org/data/quarel](https://allenai.org/data/quarel)
+- **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:** 0.60 MB
+- **Size of the generated dataset:** 1.46 MB
+- **Total amount of disk used:** 2.07 MB
+
+### Dataset Summary
+
+QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms.
+
+### 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:** 0.60 MB
+- **Size of the generated dataset:** 1.46 MB
+- **Total amount of disk used:** 2.07 MB
+
+An example of 'train' looks as follows.
+```
+{
+    "answer_index": 0,
+    "id": "QuaRel_V1_B5_1403",
+    "logical_form_pretty": "qrel(time, lower, world1) -> qrel(distance, higher, world2) ; qrel(distance, higher, world1)",
+    "logical_forms": ["(infer (time lower world1) (distance higher world2) (distance higher world1))", "(infer (time lower world2) (distance higher world1) (distance higher world2))"],
+    "question": "John and Rita are going for a run.  Rita gets tired and takes a break on the park bench.  After twenty minutes in the park, who has run farther? (A) John (B) Rita",
+    "world_literals": {
+        "world1": ["Rita"],
+        "world2": ["John"]
+    }
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `id`: a `string` feature.
+- `answer_index`: a `int32` feature.
+- `logical_forms`: a `list` of `string` features.
+- `logical_form_pretty`: a `string` feature.
+- `world_literals`: a dictionary feature containing:
+  - `world1`: a `string` feature.
+  - `world2`: a `string` feature.
+- `question`: a `string` feature.
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default| 1941|       278| 552|
+
+## 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{quarel_v1,
+    title={QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships},
+    author={Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish Sabharwal},
+    year={2018},
+    journal={arXiv:1805.05377v1}
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.