Upload batch 79 (20 files, last=huggingface_dataset/Dataset_Card/RohanAiLab_persian_news_dataset.md)

huggingface_dataset/Dataset_Card/AigizK_bashkir-russian-parallel-corpora.md

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+---
+dataset_info:
+  features:
+  - name: ba
+    dtype: string
+  - name: ru
+    dtype: string
+  - name: corpus
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 282054412
+    num_examples: 702100
+  download_size: 129601180
+  dataset_size: 282054412
+task_categories:
+- translation
+language:
+- ba
+- ru
+license: cc-by-4.0
+---
+# Dataset Card for "bashkir-russian-parallel-corpora"
+
+### How the dataset was assembled.
+
+1. find the text in two languages. it can be a translated book or an internet page (wikipedia, news site)
+2. our algorithm tries to match Bashkir sentences with their translation in Russian
+3. We give these pairs to people to check
+```
+@inproceedings{
+title={Bashkir-Russian parallel corpora},
+author={Iskander Shakirov, Aigiz Kunafin},
+year={2023}
+}
+```

huggingface_dataset/Dataset_Card/DFKI-SLT_scidtb.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license: []
+multilinguality:
+- monolingual
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- token-classification
+task_ids:
+- parsing
+pretty_name: Scientific Dependency Tree Bank
+language_bcp47:
+- en-US
+---
+
+# Dataset Card for SciDTB
+
+## 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://github.com/PKU-TANGENT/SciDTB
+- **Repository:** https://github.com/PKU-TANGENT/SciDTB
+- **Paper:** https://aclanthology.org/P18-2071/
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+SciDTB is a domain-specific discourse treebank annotated on scientific articles written in English-language. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. Furthermore, this treebank is made as a benchmark for evaluating discourse dependency parsers. This dataset can benefit many downstream NLP tasks such as machine translation and automatic summarization. 
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+English.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point consist of `root` which is a list of nodes in dependency tree. Each node in the list has four fields: `id` containing id for the node, `parent` contains id of the parent node, `text` refers to the span that is part of the current node and finally `relation` represents relation between current node and parent node.
+
+An example from SciDTB train set is given below:  
+
+```
+{
+	"root": [
+		{
+			"id": 0,
+			"parent": -1,
+			"text": "ROOT",
+			"relation": "null"
+		},
+		{
+			"id": 1,
+			"parent": 0,
+			"text": "We propose a neural network approach ",
+			"relation": "ROOT"
+		},
+		{
+			"id": 2,
+			"parent": 1,
+			"text": "to benefit from the non-linearity of corpus-wide statistics for part-of-speech ( POS ) tagging . <S>",
+			"relation": "enablement"
+		},
+		{
+			"id": 3,
+			"parent": 1,
+			"text": "We investigated several types of corpus-wide information for the words , such as word embeddings and POS tag distributions . <S>",
+			"relation": "elab-aspect"
+		},
+		{
+			"id": 4,
+			"parent": 5,
+			"text": "Since these statistics are encoded as dense continuous features , ",
+			"relation": "cause"
+		},
+		{
+			"id": 5,
+			"parent": 3,
+			"text": "it is not trivial to combine these features ",
+			"relation": "elab-addition"
+		},
+		{
+			"id": 6,
+			"parent": 5,
+			"text": "comparing with sparse discrete features . <S>",
+			"relation": "comparison"
+		},
+		{
+			"id": 7,
+			"parent": 1,
+			"text": "Our tagger is designed as a combination of a linear model for discrete features and a feed-forward neural network ",
+			"relation": "elab-aspect"
+		},
+		{
+			"id": 8,
+			"parent": 7,
+			"text": "that captures the non-linear interactions among the continuous features . <S>",
+			"relation": "elab-addition"
+		},
+		{
+			"id": 9,
+			"parent": 10,
+			"text": "By using several recent advances in the activation functions for neural networks , ",
+			"relation": "manner-means"
+		},
+		{
+			"id": 10,
+			"parent": 1,
+			"text": "the proposed method marks new state-of-the-art accuracies for English POS tagging tasks . <S>",
+			"relation": "evaluation"
+		}
+	]
+}
+```
+
+More such raw data instance can be found [here](https://github.com/PKU-TANGENT/SciDTB/tree/master/dataset)
+
+### Data Fields
+
+- id: an integer identifier for the node
+- parent: an integer identifier for the parent node
+- text: a string containing text for the current node
+- relation: a string representing discourse relation between current node and parent node
+
+### Data Splits
+
+Dataset consists of three splits: `train`, `dev` and  `test`.  
+
+| Train   | Valid | Test |
+| ------ | ----- | ---- |
+| 743 |  154 | 152|
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+More information can be found [here](https://aclanthology.org/P18-2071/)
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+```
+@inproceedings{yang-li-2018-scidtb,
+    title = "{S}ci{DTB}: Discourse Dependency {T}ree{B}ank for Scientific Abstracts",
+    author = "Yang, An  and
+      Li, Sujian",
+    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
+    month = jul,
+    year = "2018",
+    address = "Melbourne, Australia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P18-2071",
+    doi = "10.18653/v1/P18-2071",
+    pages = "444--449",
+    abstract = "Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.",
+}
+```

huggingface_dataset/Dataset_Card/Datatang_Chinese_Mandarin_Synthesis_Data_Female_Customer_Service.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/Chinese_Mandarin_Synthesis_Data_Female_Customer_Service
+
+## 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/3zRJtHc
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+26.1 Hours - Chinese Mandarin Synthesis Corpus-Female, Customer Service, It is recorded by Chinese native speakers, with lively and frindly voice. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.
+                           
+For more details, please refer to the link: https://bit.ly/3HFDs29
+
+### Supported Tasks and Leaderboards
+
+tts: The dataset can be used to train a model for Text to Speech (TTS). 
+
+### 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/LRGB_voc_superpixels_edge_wt_only_coord_10.md

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+---
+task_categories:
+- graph-ml
+size_categories:
+- 1M<n<10M
+tags:
+- lrgb
+---
+
+# `voc_superpixels_edge_wt_only_coord_10`
+
+### Dataset Summary
+
+|  Dataset | Domain  |  Task | Node Feat. (dim)  | Edge Feat. (dim) | Perf. Metric | 
+|---|---|---|---|---|---|
+| PascalVOC-SP| Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
+
+|  Dataset | # Graphs  |  # Nodes | μ Nodes  | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter 
+|---|---:|---:|---:|:---:|---:|---:|---:|---:|
+| PascalVOC-SP| 11,355 | 5,443,545 | 479.40 | 5.65 | 30,777,444 | 2,710.48 | 10.74±0.51 | 27.62±2.13 |
+
+## Additional Information
+
+### Dataset Curators
+
+* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
+
+### Licensing Information
+
+[Custom License](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/index.html) for Pascal VOC 2011 (respecting Flickr terms of use)
+
+### Citation Information
+
+```
+@article{dwivedi2022LRGB,
+  title={Long Range Graph Benchmark}, 
+  author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
+  journal={arXiv:2206.08164},
+  year={2022}
+}
+```

huggingface_dataset/Dataset_Card/RobotsMaliAI_bayelemabaga.md

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+# BAYƐLƐMABAGA: Parallel French - Bambara Dataset for Machine Learning
+
+## Overview
+The Bayelemabaga dataset is a collection of 44562 aligned machine translation ready Bambara-French lines, originating from [Corpus Bambara de Reference](http://cormande.huma-num.fr/corbama/run.cgi/first_form). The dataset is constitued of text extracted from **264** text files, varing from periodicals, books, short stories, blog posts, part of the Bible and the Quran.  
+
+## Snapshot: 46976
+|    |    |
+|:---|---:|
+| **Lines** | **46976** |
+| French Tokens (spacy) | 691312 |
+| Bambara Tokens (daba) | 660732 |
+| French Types | 32018 |
+| Bambara Types | 29382 |
+| Avg. Fr line length | 77.6 |
+| Avg. Bam line length | 61.69 |
+| Number of text sources | 264 |
+
+## Data Splits
+|       |     |       |
+|:-----:|:---:|------:|
+| Train | 80% | 37580 |
+| Valid | 10% | 4698  |
+| Test  | 10% | 4698  |
+||
+
+## Remarks
+
+* We are working on resolving some last minute misalignment issues.
+
+### Maintenance
+
+* This dataset is supposed to be actively maintained.
+
+### Benchmarks:
+
+- `Coming soon`
+
+### Sources
+
+- [`sources`](./bayelemabaga/sources.txt)
+
+### To note: 
+- ʃ => (sh/shy) sound: Symbol left in the dataset, although not a part of bambara orthography nor French orthography.
+
+## License
+
+- `CC-BY-SA-4.0`
+
+## Version
+
+- `1.0.1`
+
+## Citation
+
+```
+@misc{bayelemabagamldataset2022
+    title={Machine Learning Dataset Development for Manding Languages},
+    author={
+        Valentin Vydrin and
+        Jean-Jacques Meric and
+        Kirill Maslinsky and
+        Andrij Rovenchak and
+        Allashera Auguste Tapo and
+        Sebastien Diarra and
+        Christopher Homan and
+        Marco Zampieri and
+        Michael Leventhal
+    },
+    howpublished = {url{https://github.com/robotsmali-ai/datasets}},
+    year={2022}
+}
+```
+
+## Contacts
+- `sdiarra <at> robotsmali.org`
+- `aat3261 <at> rit.edu`

huggingface_dataset/Dataset_Card/RohanAiLab_persian_news_dataset.md

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+---
+pretty_name: persian_news_datset
+language:
+- fa
+source_datasets:
+- original
+task_categories:
+- text-classification
+- sequence-modeling
+task_ids:
+- language-modeling
+- multi-class-classification
+
+---
+# Persian_News_Dataset
+
+
+# Dataset Summary
+
+persian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. This dataset can be used in different NLP tasks like language modeling, classification, supervised topic modeling,...
+
+This effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: `free` and `easy-to-use`. Here is a quick HOW-TO for using this dataset in datasets library:[Demo-datasets](https://saied71.github.io/RohanAiLab/2021/09/03/Demo-datasets.html)
+
+# Description
+
+As discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:
+```
+text :سه‌شنبه شب از دور برگشت مرحله نیمه‌نهایی لیگ قهرمانان اروپا، منچسترسیتی در ورزشگاه «اتحاد» میزبان پاری‌سن‌ژرمن بود و با ارائه نمایشی حساب شده و تحسین برانگیز به پیروزی دو بر صفر دست یافت.بازی رفت در پاریس با برتری دو بر یک سیتی به اتمام رسیده بود و با این اوصاف تیم تحت هدایت «پپ گواردیولا» در مجموع با پیروزی چهار بر یک، راهی فینال شد.بارش برف موجب سفیدپوش شدن زمین شده بود و همین امر بر عملکرد تیم‌ها تاثیر گذاشت. دیدار در حالی آغاز به کار کرد که «امباپه» ستاره پاریسی‌ها که به تازگی از مصدومیت رهایی پیدا کرده است، نیمکت‌نشین بود.بازی با حملات میهمان آغاز شد و در دقیقه هفتم داور هلندی با تصمیمی عجیب اعتقاد داشت توپ به دست «زینچنکو» مدافع سیتی برخورد کرده و نقطه پنالتی را نشان داد، اما با استفاده از سیستم کمک داور ویدئویی، پنالتی پس گرفته شد. سیتی خیلی زود به هدفش رسید و در دقیقه ۱۰ حرکت عالی او و پاس به «دی‌بروین» موجب شد تا توپ در یک رفت و برگشت به «ریاض محرز» رسیده و این بازیکن الجزایری گل نخست بازی را برای میزبان به ارمغان آورد.در دقیقه ۱۶ ضربه سر «مارکینیوش» مدافع پیش‌تاخته پاری‌سن‌ژرمن با بدشانسی به تیرک دروازه سیتی برخورد کرد.در ادامه برای دقایقی، بازیکنان در میانه میدان خطاهای متعددی انجام دادند و این امر موجب ایجاد چند درگیری شد.هرچند نماینده فرانسه درپی جبران مافات بود اما برنامه‌ای برای رسیدن به این مهم نداشت تا نیمه نخست با همین یک گل همراه شود.در نیمه دوم هم حملات پاریسی‌ها سودی نداشت و در طرف مقابل منچسترسیتی، بازی بسیار هوشمندانه‌ای ارائه کرد.در دقیقه ۶۲ و در ضد حمله‌ای برق آسا، «فیل فودن» با پاسی عالی توپ را به «ریاض محرز» رساند تا این بازیکن گل دوم خود و تیمش را ثبت کرده و سند صعود سیتی به فینال را امضا کند.در دقیقه ۶۸ «آنخل دی‌ماریا» وینگر آرژانتینی تیم پاری‌سن‌ژرمن پس از درگیری با «فرناندینو» با کارت قرمز داور از زمین اخراج شد تا کار تیمش تمام شود.در این بازی پاری‌سن‌ژرمن با تفکرات «پوچتینو»، طراحی حملات خود را به «نیمار» سپرده بود اما این بازیکن مطرح برزیلی با حرکات انفرادی بیش از از اندازه، عملکرد خوبی نداشت و حملات تیمش را خراب کرد.در نهایت بازی با پیروزی سیتی همراه شد و مالکان ثروتمند منچسترسیتی به آرزوی خود رسیده و پس از سال‌ها سرمایه‌گذاری به دیدار نهایی رسیدند. این اولین حضور سیتی در فینال لیگ قهرمانان اروپا است.چهارشنبه شب در دیگر دیدار دور برگشت نیمه‌نهایی، چلسی انگلیس در ورزشگاه «استمفورد بریج» شهر لندن پذیرای رئال‌مادرید اسپانیا است. بازی رفت با تساوی یک بر یک به اتمام رسید
+title:آرزوی سیتی برآورده شد؛ صعود شاگردان «گواردیولا» به فینال
+category:ورزش
+```
+
+# Citation
+```
+  rohanailab@gmail.com
+  title={persian_news_dataset},
+  author={Saied Alimoradi},
+  year={2021}
+}
+```

huggingface_dataset/Dataset_Card/anli.md

@@ -0,0 +1,241 @@
+---
+annotations_creators:
+- crowdsourced
+- machine-generated
+language_creators:
+- found
+language:
+- en
+license:
+- cc-by-nc-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+- extended|hotpot_qa
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+- multi-input-text-classification
+paperswithcode_id: anli
+pretty_name: Adversarial NLI
+dataset_info:
+  features:
+  - name: uid
+    dtype: string
+  - name: premise
+    dtype: string
+  - name: hypothesis
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': entailment
+          '1': neutral
+          '2': contradiction
+  - name: reason
+    dtype: string
+  config_name: plain_text
+  splits:
+  - name: train_r1
+    num_bytes: 8006920
+    num_examples: 16946
+  - name: dev_r1
+    num_bytes: 573444
+    num_examples: 1000
+  - name: test_r1
+    num_bytes: 574933
+    num_examples: 1000
+  - name: train_r2
+    num_bytes: 20801661
+    num_examples: 45460
+  - name: dev_r2
+    num_bytes: 556082
+    num_examples: 1000
+  - name: test_r2
+    num_bytes: 572655
+    num_examples: 1000
+  - name: train_r3
+    num_bytes: 44720895
+    num_examples: 100459
+  - name: dev_r3
+    num_bytes: 663164
+    num_examples: 1200
+  - name: test_r3
+    num_bytes: 657602
+    num_examples: 1200
+  download_size: 18621352
+  dataset_size: 77127356
+---
+
+# Dataset Card for "anli"
+
+## 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/facebookresearch/anli/](https://github.com/facebookresearch/anli/)
+- **Paper:** [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://arxiv.org/abs/1910.14599)
+- **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:** 17.76 MB
+- **Size of the generated dataset:** 73.55 MB
+- **Total amount of disk used:** 91.31 MB
+
+### Dataset Summary
+
+The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
+The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
+ANLI is much more difficult than its predecessors including SNLI and MNLI.
+It contains three rounds. Each round has train/dev/test splits.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+#### plain_text
+
+- **Size of downloaded dataset files:** 17.76 MB
+- **Size of the generated dataset:** 73.55 MB
+- **Total amount of disk used:** 91.31 MB
+
+An example of 'train_r2' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.",
+    "label": 0,
+    "premise": "\"Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...",
+    "reason": "",
+    "uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### plain_text
+- `uid`: a `string` feature.
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+- `reason`: a `string` feature.
+
+### Data Splits
+
+|   name   |train_r1|dev_r1|train_r2|dev_r2|train_r3|dev_r3|test_r1|test_r2|test_r3|
+|----------|-------:|-----:|-------:|-----:|-------:|-----:|------:|------:|------:|
+|plain_text|   16946|  1000|   45460|  1000|  100459|  1200|   1000|   1000|   1200|
+
+## 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
+
+[cc-4 Attribution-NonCommercial](https://github.com/facebookresearch/anli/blob/main/LICENSE)
+
+### Citation Information
+
+```
+@InProceedings{nie2019adversarial,
+    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
+    author={Nie, Yixin
+                and Williams, Adina
+                and Dinan, Emily
+                and Bansal, Mohit
+                and Weston, Jason
+                and Kiela, Douwe},
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@easonnie](https://github.com/easonnie), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-lener_br-lener_br-c186f5-1776861661.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- lener_br
+eval_info:
+  task: entity_extraction
+  model: Luciano/xlm-roberta-base-finetuned-lener-br
+  metrics: []
+  dataset_name: lener_br
+  dataset_config: lener_br
+  dataset_split: train
+  col_mapping:
+    tokens: tokens
+    tags: ner_tags
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: Luciano/xlm-roberta-base-finetuned-lener-br
+* Dataset: lener_br
+* Config: lener_br
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_cot-mathema-acb860-1886064281.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- mathemakitten/winobias_antistereotype_test_cot
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-13b
+  metrics: []
+  dataset_name: mathemakitten/winobias_antistereotype_test_cot
+  dataset_config: mathemakitten--winobias_antistereotype_test_cot
+  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: facebook/opt-13b
+* Dataset: mathemakitten/winobias_antistereotype_test_cot
+* Config: mathemakitten--winobias_antistereotype_test_cot
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906069.md

@@ -0,0 +1,35 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: deepset/xlm-roberta-large-squad2
+  metrics: ['bertscore']
+  dataset_name: squad_v2
+  dataset_config: squad_v2
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: deepset/xlm-roberta-large-squad2
+* Dataset: squad_v2
+* Config: squad_v2
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model.

huggingface_dataset/Dataset_Card/carlosdanielhernandezmena_ravnursson_asr.md

@@ -0,0 +1,219 @@
+---
+annotations_creators:
+- expert-generated
+language:
+- fo
+language_creators:
+- expert-generated
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags:
+- faroe islands
+- faroese
+- ravnur project
+- speech recognition in faroese
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+---
+
+# Dataset Card for ravnursson_asr
+## 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-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:** [Ravnursson Faroese Speech and Transcripts](http://hdl.handle.net/20.500.12537/276)
+- **Repository:** [Clarin.is](http://hdl.handle.net/20.500.12537/276)
+- **Paper:** [Creating a basic language resource kit for faroese.](https://aclanthology.org/2022.lrec-1.495.pdf)
+- **Point of Contact:** [Annika Simonsen](mailto:annika.simonsen@hotmail.com), [Carlos Mena](mailto:carlos.mena@ciempiess.org)
+
+### Dataset Summary
+The corpus "RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS" (or RAVNURSSON Corpus for short) is a collection of speech recordings with transcriptions intended for Automatic Speech Recognition (ASR) applications in the language that is spoken at the Faroe Islands (Faroese). It was curated at the Reykjavík University (RU) in 2022.
+
+The RAVNURSSON Corpus is an extract of the "Basic Language Resource Kit 1.0" (BLARK 1.0) [1] developed by the Ravnur Project from the Faroe Islands [2]. As a matter of fact, the name RAVNURSSON comes from Ravnur (a tribute to the Ravnur Project) and the suffix "son" which in Icelandic means "son of". Therefore, the name "RAVNURSSON" means "The (Icelandic) son of Ravnur". The double "ss" is just for aesthetics.
+
+The audio was collected by recording speakers reading texts. The participants are aged 15-83, divided into 3 age groups: 15-35, 36-60 and 61+.
+
+The speech files are made of 249 female speakers and 184 male speakers; 433 speakers total. The recordings were made on TASCAM DR-40 Linear PCM audio recorders using the built-in stereo microphones in WAVE 16 bit with a sample rate of 48kHz, but then, downsampled to 16kHz@16bit mono for this corpus.
+
+[1] Simonsen, A., Debess, I. N., Lamhauge, S. S., & Henrichsen, P. J. Creating a basic language resource kit for Faroese. In LREC 2022. 13th International Conference on Language Resources and Evaluation.
+    
+[2] Website. The Project Ravnur under the Talutøkni Foundation https://maltokni.fo/en/the-ravnur-project
+
+### Example Usage
+The RAVNURSSON Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:
+```python
+from datasets import load_dataset
+ravnursson = load_dataset("carlosdanielhernandezmena/ravnursson_asr")
+```
+To load an specific split (for example, the validation split) do:
+```python
+from datasets import load_dataset
+ravnursson = load_dataset("carlosdanielhernandezmena/ravnursson_asr",split="validation")
+```
+
+### Supported Tasks
+automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
+
+### Languages
+The audio is in Faroese.
+The reading prompts for the RAVNURSSON Corpus have been generated by expert linguists. The whole corpus was balanced for phonetic and dialectal coverage; Test and Dev subsets are gender-balanced. Tabular computer-searchable information is included as well as written documentation.
+
+## Dataset Structure
+
+### Data Instances
+```python
+{
+  'audio_id': 'KAM06_151121_0101', 
+  'audio': {
+    'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/32b4a757027b72b8d2e25cd9c8be9c7c919cc8d4eb1a9a899e02c11fd6074536/dev/RDATA2/KAM06_151121/KAM06_151121_0101.flac', 
+    'array': array([ 0.0010376 , -0.00521851, -0.00393677, ...,  0.00128174,
+        0.00076294,  0.00045776], dtype=float32), 
+    'sampling_rate': 16000
+  }, 
+  'speaker_id': 'KAM06_151121', 
+  'gender': 'female', 
+  'age': '36-60', 
+  'duration': 4.863999843597412, 
+  'normalized_text': 'endurskin eru týdningarmikil í myrkri', 
+  'dialect': 'sandoy'
+}
+```
+
+### Data Fields
+* `audio_id` (string) - id of audio segment
+* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
+* `speaker_id` (string) - id of speaker
+* `gender` (string) - gender of speaker (male or female)
+* `age` (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+).
+* `duration` (float32) - duration of the audio file in seconds.
+* `normalized_text` (string) - normalized audio segment transcription
+* `dialect` (string) - dialect group, for example "Suðuroy" or "Sandoy".
+
+### Data Splits
+The speech material has been subdivided into portions for training (train), development (evaluation) and testing (test). Lengths of each portion are: train = 100h08m, test = 4h30m, dev (evaluation)=4h30m.
+
+To load an specific portion please see the above section "Example Usage".
+
+The development and test portions have exactly 10 male and 10 female speakers each and both portions have exactly the same size in hours (4.5h each).
+
+## Dataset Creation
+
+### Curation Rationale
+
+The directory called "speech" contains all the speech files of the corpus. The files in the speech directory are divided in three directories: train, dev and test. The train portion is sub-divided in three types of recordings: RDATA1O, RDATA1OP and RDATA2; this is due to the organization of the recordings in the original BLARK 1.0. There, the recordings are divided in Rdata1 and Rdata2.
+
+One main difference between Rdata1 and Rdata2 is that the reading environment for Rdata2 was controlled by a software called "PushPrompt" which is included in the original BLARK 1.0. Another main difference is that in Rdata1 there are some available transcriptions labeled at the phoneme level. For this reason the audio files in the speech directory of the RAVNURSSON corpus are divided in the folders RDATA1O where "O" is for "Orthographic" and RDATA1OP where "O" is for Orthographic and "P" is for phonetic.
+
+In the case of the dev and test portions, the data come only from Rdata2 which does not have labels at the phonetic level.
+
+It is important to clarify that the RAVNURSSON Corpus only includes transcriptions at the orthographic level.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+The dataset was released with normalized text only at an orthographic level in lower-case. The normalization process was performed by automatically removing punctuation marks and characters that are not present in the Faroese alphabet.
+
+#### Who are the source language producers?
+
+* The utterances were recorded using a TASCAM DR-40.
+
+* Participants self-reported their age group, gender, native language and dialect.
+
+* Participants are aged between 15 to 83 years. 
+ 
+* The corpus contains 71949 speech files from 433 speakers, totalling 109 hours and 9 minutes.
+
+### Annotations
+
+#### Annotation process
+
+Most of the reading prompts were selected by experts from a Faroese text corpus (news, blogs, Wikipedia etc.) and were edited to fit the format. Reading prompts that are within specific domains (such as Faroese place names, numbers, license plates, telling time etc.) were written by the Ravnur Project. Then, a software tool called PushPrompt were used for reading sessions (voice recordings). PushPromt presents the text items in the reading material to the reader, allowing him/her to manage the session interactively (adjusting the reading tempo, repeating speech productions at wish, inserting short breaks as needed, etc.). When the reading session is completed, a log file (with time stamps for each production) is written as a data table compliant with the TextGrid-format. 
+
+#### Who are the annotators?
+The corpus was annotated by the [Ravnur Project](https://maltokni.fo/en/the-ravnur-project)
+
+### Personal and Sensitive Information
+The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+This is the first ASR corpus in Faroese.
+
+### Discussion of Biases
+As the number of reading prompts was limited, the common denominator in the RAVNURSSON corpus is that one prompt is read by more than one speaker. This is relevant because is a common practice in ASR to create a language model using the prompts that are found in the train portion of the corpus. That is not recommended for the RAVNURSSON Corpus as it counts with many prompts shared by all the portions and that will produce an important bias in the language modeling task.
+
+In this section we present some statistics about the repeated prompts through all the portions of the corpus.
+
+- In the train portion:
+	* Total number of prompts = 65616
+	* Number of unique prompts = 38646
+There are 26970 repeated prompts in the train portion. In other words, 41.1% of the prompts are repeated.
+
+- In the test portion:
+	* Total number of prompts = 3002
+	* Number of unique prompts = 2887
+There are 115 repeated prompts in the test portion. In other words, 3.83% of the prompts are repeated.
+
+- In the dev portion:
+	* Total number of prompts = 3331
+	* Number of unique prompts = 3302
+There are 29 repeated prompts in the dev portion. In other words, 0.87% of the prompts are repeated.
+
+- Considering the corpus as a whole:
+	* Total number of prompts = 71949
+	* Number of unique prompts = 39945
+There are 32004 repeated prompts in the whole corpus. In other words, 44.48% of the prompts are repeated.
+
+NOTICE!: It is also important to clarify that none of the 3 portions of the corpus share speakers.
+
+### Other Known Limitations
+"RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS" by Carlos Daniel Hernández Mena and Annika Simonsen is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+
+## Additional Information
+### Dataset Curators
+The dataset was collected by Annika Simonsen and curated by Carlos Daniel Hernández Mena.
+### Licensing Information
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
+### Citation Information
+```
+@misc{carlosmenaravnursson2022,
+      title={Ravnursson Faroese Speech and Transcripts}, 
+      author={Hernandez Mena, Carlos Daniel and Simonsen, Annika},
+      year={2022},
+      url={http://hdl.handle.net/20.500.12537/276},
+}
+```
+### Contributions
+This project was made possible under the umbrella of the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.
+
+Special thanks to Dr. Jón Guðnason, professor at Reykjavík University and head of the Language and Voice Lab (LVL) for providing computational resources.

huggingface_dataset/Dataset_Card/djghosh_wds_vtab-cifar100_test.md

@@ -0,0 +1,15 @@
+# CIFAR-100 Webdataset (Test set only)
+
+Original paper: [Learning Multiple Layers of Features from Tiny Images](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)
+
+Homepage: https://www.cs.toronto.edu/~kriz/cifar.html
+
+Bibtex:
+```
+@TECHREPORT{Krizhevsky09learningmultiple,
+    author = {Alex Krizhevsky},
+    title = {Learning multiple layers of features from tiny images},
+    institution = {},
+    year = {2009}
+}
+```

huggingface_dataset/Dataset_Card/facebook_voxpopuli.md

@@ -0,0 +1,294 @@
+---
+annotations_creators: []
+language:
+- en
+- de
+- fr
+- es
+- pl
+- it
+- ro
+- hu
+- cs
+- nl
+- fi
+- hr
+- sk
+- sl
+- et
+- lt
+language_creators: []
+license:
+- cc0-1.0
+- other
+multilinguality:
+- multilingual
+pretty_name: VoxPopuli
+size_categories: []
+source_datasets: []
+tags: []
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+---
+
+# Dataset Card for Voxpopuli
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/facebookresearch/voxpopuli
+- **Repository:** https://github.com/facebookresearch/voxpopuli
+- **Paper:** https://arxiv.org/abs/2101.00390
+- **Point of Contact:** [changhan@fb.com](mailto:changhan@fb.com), [mriviere@fb.com](mailto:mriviere@fb.com), [annl@fb.com](mailto:annl@fb.com)
+
+### Dataset Summary
+
+VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
+The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home). We acknowledge the European Parliament for creating and sharing these materials.
+This implementation contains transcribed speech data for 18 languages.
+It also contains 29 hours of transcribed speech data of non-native English intended for research in ASR for accented speech (15 L2 accents)
+
+### Example usage
+
+VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name:
+
+```python
+from datasets import load_dataset
+
+voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr")
+```
+
+To load all the languages in a single dataset use "multilang" config name:
+
+```python
+voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang")
+```
+
+To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter:
+
+```python
+voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"])
+```
+
+To load accented English data, use "en_accented" config name:
+
+```python
+voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented")
+```
+
+**Note that L2 English subset contains only `test` split.**
+
+
+### Supported Tasks and Leaderboards
+
+* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
+
+Accented English subset can also be used for research in ASR for accented speech (15 L2 accents)
+
+### Languages
+
+VoxPopuli contains labelled (transcribed) data for 18 languages:
+
+| Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
+|:---:|:---:|:---:|:---:|:---:|
+| English | En | 543 | 1313 | 4.8M |
+| German | De | 282 | 531 | 2.3M |
+| French | Fr | 211 | 534 | 2.1M |
+| Spanish | Es | 166 | 305 | 1.6M |
+| Polish | Pl | 111 | 282 | 802K |
+| Italian | It | 91 | 306 | 757K |
+| Romanian | Ro | 89 | 164 | 739K |
+| Hungarian | Hu | 63 | 143 | 431K |
+| Czech | Cs | 62 | 138 | 461K |
+| Dutch | Nl | 53 | 221 | 488K |
+| Finnish | Fi | 27 | 84 | 160K |
+| Croatian | Hr | 43 | 83 | 337K |
+| Slovak | Sk | 35 | 96 | 270K |
+| Slovene | Sl | 10 | 45 | 76K |
+| Estonian | Et | 3 | 29 | 18K |
+| Lithuanian | Lt | 2 | 21 | 10K |
+| Total | | 1791 | 4295 | 15M |
+
+
+Accented speech transcribed data has 15 various L2 accents:
+
+| Accent | Code | Transcribed Hours | Transcribed Speakers |
+|:---:|:---:|:---:|:---:|
+| Dutch | en_nl | 3.52 | 45 |
+| German | en_de | 3.52 | 84 |
+| Czech | en_cs | 3.30 | 26 |
+| Polish | en_pl | 3.23 | 33 |
+| French | en_fr | 2.56 | 27 |
+| Hungarian | en_hu | 2.33 | 23 |
+| Finnish | en_fi | 2.18 | 20 |
+| Romanian | en_ro | 1.85 | 27 |
+| Slovak | en_sk | 1.46 | 17 |
+| Spanish | en_es | 1.42 | 18 |
+| Italian | en_it | 1.11 | 15 |
+| Estonian | en_et | 1.08 | 6 |
+| Lithuanian | en_lt | 0.65 | 7 |
+| Croatian | en_hr | 0.42 | 9 |
+| Slovene | en_sl | 0.25 | 7 |
+
+## Dataset Structure
+
+### Data Instances
+
+```python
+{
+  'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5',
+  'language': 11,  # "hr"
+  'audio': {
+    'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav',
+    'array': array([-0.01434326, -0.01055908,  0.00106812, ...,  0.00646973], dtype=float32),
+    'sampling_rate': 16000
+  },
+  'raw_text': '',
+  'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.',
+  'gender': 'female',
+  'speaker_id': '119431',
+  'is_gold_transcript': True,
+  'accent': 'None'
+}
+```
+
+### Data Fields
+
+* `audio_id` (string) - id of audio segment
+* `language` (datasets.ClassLabel) - numerical id of audio segment 
+* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
+* `raw_text` (string) - original (orthographic) audio segment text
+* `normalized_text` (string) - normalized audio segment transcription
+* `gender` (string) - gender of speaker
+* `speaker_id` (string) - id of speaker
+* `is_gold_transcript` (bool) - ?
+* `accent` (string) - type of accent, for example "en_lt", if applicable, else "None".
+
+### Data Splits
+
+All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home)
+
+#### Initial Data Collection and Normalization
+
+The VoxPopuli transcribed set comes from aligning  the full-event source speech audio with the transcripts for plenary sessions. Official timestamps
+are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture
+of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps,
+we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation. 
+Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available.
+
+The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a
+maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts.
+The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data.
+
+The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment. 
+We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER).
+
+#### Who are the source language producers?
+
+Speakers are participants of the European Parliament events, many of them are EU officials.
+
+### 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
+
+Gender speakers distribution is imbalanced, percentage of female speakers is mostly lower than 50% across languages, with the minimum of 15% for the Lithuanian language data.
+
+VoxPopuli includes all available speeches from the 2009-2020 EP events without any selections on the topics or speakers.
+The speech contents represent the standpoints of the speakers in the EP events, many of which are EU officials.
+
+
+### Other Known Limitations
+
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The dataset is distributet under CC0 license, see also [European Parliament's legal notice](https://www.europarl.europa.eu/legal-notice/en/) for the raw data.
+
+### Citation Information
+
+Please cite this paper:
+
+```bibtex
+@inproceedings{wang-etal-2021-voxpopuli,
+    title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation",
+    author = "Wang, Changhan  and
+      Riviere, Morgane  and
+      Lee, Ann  and
+      Wu, Anne  and
+      Talnikar, Chaitanya  and
+      Haziza, Daniel  and
+      Williamson, Mary  and
+      Pino, Juan  and
+      Dupoux, Emmanuel",
+    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.acl-long.80",
+    pages = "993--1003",
+}
+```
+
+### Contributions
+
+Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

huggingface_dataset/Dataset_Card/irds_disks45_nocr_trec7.md

@@ -0,0 +1,56 @@
+---
+pretty_name: '`disks45/nocr/trec7`'
+viewer: false
+source_datasets: ['irds/disks45_nocr']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `disks45/nocr/trec7`
+
+The `disks45/nocr/trec7` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/disks45#disks45/nocr/trec7).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=50
+ - `qrels`: (relevance assessments); count=80,345
+
+ - For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/disks45_nocr_trec7', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
+
+qrels = load_dataset('irds/disks45_nocr_trec7', '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
+
+```
+@misc{Voorhees1996Disks45,
+  title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
+  author = {Ellen M. Voorhees},
+  doi = {10.18434/t47g6m},
+  year = {1996},
+  publisher = {National Institute of Standards and Technology}
+}
+@inproceedings{Voorhees1998Trec7,
+  title = {Overview of the Seventh Text Retrieval Conference (TREC-7)},
+  author = {Ellen M. Voorhees and Donna Harman},
+  year = {1998},
+  booktitle = {TREC}
+}
+```

huggingface_dataset/Dataset_Card/lmqg_qg_subjqa.md

@@ -0,0 +1,91 @@
+---
+license: cc-by-4.0
+pretty_name: SubjQA for question generation
+language: en
+multilinguality: monolingual
+size_categories: 10K<n<100K
+source_datasets: subjqa
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+tags:
+- question-generation
+---
+
+# Dataset Card for "lmqg/qg_subjqa"
+
+## 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 a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
+ ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
+Modified version of [SubjQA](https://github.com/megagonlabs/SubjQA) for question generation (QG) task.
+
+### Supported Tasks and Leaderboards
+* `question-generation`: The dataset can be used to train a model for question 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.
+```
+{
+  "question": "How is book?",
+  "paragraph": "I am giving "Gone Girl" 3 stars, but only begrudgingly. In my mind, any book that takes me 3 months and 20 different tries to read is not worth 3 stars, especially a book written by an author I already respect. And I am not kidding, for me the first half of "Gone Girl" was a PURE TORTURE to read.Amy Dunn disappears on the day of her 5th wedding anniversary. All gradually uncovered evidence suggests that her husband, Nick, is somehow involved. Did he kill her? Was she kidnapped? What happened to Amy? One thing is clear, Nick and Amy's marriage wasn't as perfect as everybody thought.The first part of the novel is all about the investigation into Amy's disappearance, slow unraveling of Nick's dirty secrets, reminiscing about the troubled history of Nick and Amy's marriage as told in Amy's hidden diary. I strained and strained my brain trying to understand why this chunk of Gone Girl had no appeal to me whatsoever. The only answer I have is this: I am really not into reading about rich white people's problems. You want to whine to me about your dwindling trust fund? Losing your cushy New York job? Moving south and "only" renting a mansion there? Being unhappy because you have too much free time on your hands and you are used to only work as a hobby? You want to make fun of your lowly, un-posh neighbors and their casseroles? Well, I am not interested. I'd rather read about someone not necessarily likable, but at least worthy of my empathy, not waste my time on self-centered, spoiled, pathetic people who don't know what real problems are. Granted, characters in Flynn's previous novels ("Sharp Objects" and "Dark Places") are pretty pathetic and and at times revolting too, but I always felt some strange empathy towards them, not annoyance and boredom, like I felt reading about Amy and Nick's marriage voes.But then second part, with its wicked twist, changed everything. The story became much more exciting, dangerous and deranged. The main characters revealed sides to them that were quite shocking and VERY entertaining. I thought the Gillian Flynn I knew before finally unleashed her talent for writing utterly unlikable and crafty women. THEN I got invested in the story, THEN I cared.Was it too little too late though? I think it was. Something needed to be done to make "Gone Girl" a better read. Make it shorter? Cut out first part completely? I don't know. But because of my uneven experience with this novel I won't be able to recommend "Gone Girl" as readily as I did Flynn's earlier novels, even though I think this horror marriage story (it's not a true mystery, IMO) has some brilliantly written psycho goodness in it and an absolutely messed up ending that many loathed but I LOVED. I wish it didn't take so much time and patience to get to all of that...",
+  "answer": "any book that takes me 3 months and 20 different tries to read is not worth 3 stars",
+  "sentence": "In my mind, any book that takes me 3 months and 20 different tries to read is not worth 3 stars , especially a book written by an author I already respect.",
+  "paragraph_sentence": "I am giving "Gone Girl" 3 stars, but only begrudgingly. <hl> In my mind, any book that takes me 3 months and 20 different tries to read is not worth 3 stars , especially a book written by an author I already respect. <hl> And I am not kidding, for me the first half of "Gone Girl" was a PURE TORTURE to read. Amy Dunn disappears on the day of her 5th wedding anniversary. All gradually uncovered evidence suggests that her husband, Nick, is somehow involved. Did he kill her? Was she kidnapped? What happened to Amy? One thing is clear, Nick and Amy's marriage wasn't as perfect as everybody thought. The first part of the novel is all about the investigation into Amy's disappearance, slow unraveling of Nick's dirty secrets, reminiscing about the troubled history of Nick and Amy's marriage as told in Amy's hidden diary. I strained and strained my brain trying to understand why this chunk of Gone Girl had no appeal to me whatsoever. The only answer I have is this: I am really not into reading about rich white people's problems. You want to whine to me about your dwindling trust fund? Losing your cushy New York job? Moving south and "only" renting a mansion there? Being unhappy because you have too much free time on your hands and you are used to only work as a hobby? You want to make fun of your lowly, un-posh neighbors and their casseroles? Well, I am not interested. I'd rather read about someone not necessarily likable, but at least worthy of my empathy, not waste my time on self-centered, spoiled, pathetic people who don't know what real problems are. Granted, characters in Flynn's previous novels ("Sharp Objects" and "Dark Places") are pretty pathetic and and at times revolting too, but I always felt some strange empathy towards them, not annoyance and boredom, like I felt reading about Amy and Nick's marriage voes. But then second part, with its wicked twist, changed everything. The story became much more exciting, dangerous and deranged. The main characters revealed sides to them that were quite shocking and VERY entertaining. I thought the Gillian Flynn I knew before finally unleashed her talent for writing utterly unlikable and crafty women. THEN I got invested in the story, THEN I cared. Was it too little too late though? I think it was. Something needed to be done to make "Gone Girl" a better read. Make it shorter? Cut out first part completely? I don't know. But because of my uneven experience with this novel I won't be able to recommend "Gone Girl" as readily as I did Flynn's earlier novels, even though I think this horror marriage story (it's not a true mystery, IMO) has some brilliantly written psycho goodness in it and an absolutely messed up ending that many loathed but I LOVED. I wish it didn't take so much time and patience to get to all of that...",
+  "paragraph_answer": "I am giving "Gone Girl" 3 stars, but only begrudgingly. In my mind, <hl> any book that takes me 3 months and 20 different tries to read is not worth 3 stars <hl>, especially a book written by an author I already respect. And I am not kidding, for me the first half of "Gone Girl" was a PURE TORTURE to read.Amy Dunn disappears on the day of her 5th wedding anniversary. All gradually uncovered evidence suggests that her husband, Nick, is somehow involved. Did he kill her? Was she kidnapped? What happened to Amy? One thing is clear, Nick and Amy's marriage wasn't as perfect as everybody thought.The first part of the novel is all about the investigation into Amy's disappearance, slow unraveling of Nick's dirty secrets, reminiscing about the troubled history of Nick and Amy's marriage as told in Amy's hidden diary. I strained and strained my brain trying to understand why this chunk of Gone Girl had no appeal to me whatsoever. The only answer I have is this: I am really not into reading about rich white people's problems. You want to whine to me about your dwindling trust fund? Losing your cushy New York job? Moving south and "only" renting a mansion there? Being unhappy because you have too much free time on your hands and you are used to only work as a hobby? You want to make fun of your lowly, un-posh neighbors and their casseroles? Well, I am not interested. I'd rather read about someone not necessarily likable, but at least worthy of my empathy, not waste my time on self-centered, spoiled, pathetic people who don't know what real problems are. Granted, characters in Flynn's previous novels ("Sharp Objects" and "Dark Places") are pretty pathetic and and at times revolting too, but I always felt some strange empathy towards them, not annoyance and boredom, like I felt reading about Amy and Nick's marriage voes.But then second part, with its wicked twist, changed everything. The story became much more exciting, dangerous and deranged. The main characters revealed sides to them that were quite shocking and VERY entertaining. I thought the Gillian Flynn I knew before finally unleashed her talent for writing utterly unlikable and crafty women. THEN I got invested in the story, THEN I cared.Was it too little too late though? I think it was. Something needed to be done to make "Gone Girl" a better read. Make it shorter? Cut out first part completely? I don't know. But because of my uneven experience with this novel I won't be able to recommend "Gone Girl" as readily as I did Flynn's earlier novels, even though I think this horror marriage story (it's not a true mystery, IMO) has some brilliantly written psycho goodness in it and an absolutely messed up ending that many loathed but I LOVED. I wish it didn't take so much time and patience to get to all of that...",
+  "sentence_answer": "In my mind, <hl> any book that takes me 3 months and 20 different tries to read is not worth 3 stars <hl> , especially a book written by an author I already respect.",
+  "paragraph_id": "1b7cc3db9ec681edd253a41a2785b5a9",
+  "question_subj_level": 1,
+  "answer_subj_level": 1,
+  "domain": "books"
+}
+```
+
+The data fields are the same among all splits.
+- `question`: a `string` feature. 
+- `paragraph`: a `string` feature.
+- `answer`: a `string` feature.
+- `sentence`: a `string` feature.
+- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
+- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
+- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
+
+Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
+but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and 
+`paragraph_sentence` feature is for sentence-aware question generation.
+
+### Data Splits
+
+|   name      |train|validation|test |
+|-------------|----:|---------:|----:|
+|default (all)|4437 |     659  |1489 |
+| books       |636  |     91   |190  |
+| electronics |696  |     98   |237  |
+| movies      |723  |     100  |153  |
+| grocery     |686  |     100  |378  |
+| restaurants |822  |     128  |135  |
+| tripadvisor |874  |     142  |396  |
+
+## 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/multi_nli_mismatch.md

@@ -0,0 +1,215 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+- found
+language:
+- en
+license:
+- cc-by-3.0
+- cc-by-sa-3.0
+- mit
+- other
+license_details: Open Portion of the American National Corpus
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+- multi-input-text-classification
+paperswithcode_id: multinli
+pretty_name: Multi-Genre Natural Language Inference
+dataset_info:
+  features:
+  - name: premise
+    dtype: string
+  - name: hypothesis
+    dtype: string
+  - name: label
+    dtype: string
+  config_name: plain_text
+  splits:
+  - name: train
+    num_bytes: 75601459
+    num_examples: 392702
+  - name: validation
+    num_bytes: 2009444
+    num_examples: 10000
+  download_size: 226850426
+  dataset_size: 77610903
+---
+
+# Dataset Card for Multi-Genre Natural Language Inference (Mismatched only)
+
+## 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.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/)
+- **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:** 216.34 MB
+- **Size of the generated dataset:** 74.02 MB
+- **Total amount of disk used:** 290.36 MB
+
+### Dataset Summary
+
+The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
+crowd-sourced collection of 433k sentence pairs annotated with textual
+entailment information. The corpus is modeled on the SNLI corpus, but differs in
+that covers a range of genres of spoken and written text, and supports a
+distinctive cross-genre generalization evaluation. The corpus served as the
+basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
+
+### 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
+
+#### plain_text
+
+- **Size of downloaded dataset files:** 216.34 MB
+- **Size of the generated dataset:** 74.02 MB
+- **Total amount of disk used:** 290.36 MB
+
+An example of 'train' looks as follows.
+```
+{
+    "hypothesis": "independence",
+    "label": "contradiction",
+    "premise": "correlation"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### plain_text
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `label`: a `string` feature.
+
+### Data Splits
+
+|   name   |train |validation|
+|----------|-----:|---------:|
+|plain_text|392702|     10000|
+
+## 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{N18-1101,
+  author = "Williams, Adina
+            and Nangia, Nikita
+            and Bowman, Samuel",
+  title = "A Broad-Coverage Challenge Corpus for
+           Sentence Understanding through Inference",
+  booktitle = "Proceedings of the 2018 Conference of
+               the North American Chapter of the
+               Association for Computational Linguistics:
+               Human Language Technologies, Volume 1 (Long
+               Papers)",
+  year = "2018",
+  publisher = "Association for Computational Linguistics",
+  pages = "1112--1122",
+  location = "New Orleans, Louisiana",
+  url = "http://aclweb.org/anthology/N18-1101"
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

huggingface_dataset/Dataset_Card/mvarma_medwiki.md

@@ -0,0 +1,190 @@
+---
+YAML tags:
+annotations_creators:
+- machine-generated
+language_creators:
+- crowdsourced
+language:
+- en-US
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: medwiki
+size_categories:
+- unknown
+source_datasets:
+- extended|wikipedia
+task_categories:
+- text-retrieval
+task_ids:
+- entity-linking-retrieval
+---
+
+# Dataset Card for MedWiki
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** [Github](https://github.com/HazyResearch/medical-ned-integration)
+- **Paper:** [Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text](https://arxiv.org/abs/2110.08228)
+- **Point of Contact:** [Maya Varma](mailto:mvarma2@stanford.edu)
+
+### Dataset Summary
+
+MedWiki is a large sentence dataset collected from a medically-relevant subset of Wikipedia and annotated with biomedical entities in the Unified Medical Language System (UMLS) knowledge base. For each entity, we include a rich set of types sourced from both UMLS and WikiData. Consisting of over 13 million sentences and 17 million entity annotations, MedWiki can be utilized as a pretraining resource for language models and can improve performance of medical named entity recognition and disambiguation systems, especially on rare entities. 
+
+Here, we include two configurations of MedWiki (further details in [Dataset Creation](#dataset-creation)):
+- `MedWiki-Full` is a large sentence dataset with UMLS medical entity annotations generated through the following two steps: (1) a weak labeling proecedure to annotate WikiData entities in sentences and (2) a data integration approach that maps WikiData entities to their counterparts in UMLS.
+- `MedWiki-HQ` is a subset of MedWiki-Full with higher quality labels designed to limit noise that arises from the annotation procedure listed above.
+
+### Languages
+
+The text in the dataset is in English and was obtained from English Wikipedia.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point includes a sentence collected from Wikipedia annotated with UMLS medical entities and associated titles and types. 
+
+An example from the MedWiki test set looks as follows:
+```
+{'sent_idx_unq': 57000409,
+ 'sentence': "The hair , teeth , and skeletal side effects of TDO are lifelong , and treatment is used to manage those effects .",
+ 'mentions': ['tdo'],
+ 'entities': ['C2931236'],
+ 'entity_titles': ['Tricho-dento-osseous syndrome 1'],
+ 'types': [['Disease or Syndrome', 'disease', 'rare disease', 'developmental defect during embryogenesis', 'malformation syndrome with odontal and/or periodontal component', 'primary bone dysplasia with increased bone density', 'syndromic hair shaft abnormality']],
+ 'spans': [[10, 11]]}
+```
+
+### Data Fields
+
+- `sent_idx_unq`: a unique integer identifier for the data instance
+- `sentence`: a string sentence collected from English Wikipedia. Punctuation is separated from words, and the sentence can be tokenized into word-pieces with the .split() method.
+- `mentions`: list of medical mentions in the sentence.
+- `entities`: list of UMLS medical entity identifiers corresponding to mentions. There is exactly one entity for each mention, and the length of the `entities` list is equal to the length of the `mentions` list.
+- `entity_titles`: List of English titles collected from UMLS that describe each entity. The length of the `entity_titles` list is equal to the length of the `entities` list.
+- `types`: List of category types associated with each entity, including types collected from UMLS and WikiData.
+- `spans`: List of integer pairs representing the word span of each mention in the sentence.
+
+### Data Splits
+
+MedWiki includes two configurations: MedWiki-Full and MedWiki-HQ (described further in [Dataset Creation](#dataset-creation)). For each configuration, data is split into training, development, and test sets. The split sizes are as follow:
+
+|                             | Train | Dev | Test |
+| -----                       | ------ | ----- | ---- |
+| MedWiki-Full Sentences      |11,784,235 | 649,132 | 648,608 |
+| MedWiki-Full Mentions       |15,981,347 | 876,586 | 877,090 |
+| MedWiki-Full Unique Entities | 230,871 | 55,002 | 54,772 |
+| MedWiki-HQ Sentences        | 2,962,089 | 165,941 | 164,193 |
+| MedWiki-HQ Mentions         | 3,366,108 | 188,957 | 186,622 |
+| MedWiki-HQ Unique Entities      | 118,572 | 19,725 | 19,437 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+Existing medical text datasets are generally limited in scope, often obtaining low coverage over the entities and structural resources in the UMLS medical knowledge base. When language models are trained across such datasets, the lack of adequate examples may prevent models from learning the complex reasoning patterns that are necessary for performing effective entity linking or disambiguation, especially for rare entities as shown in prior work by [Orr et al.](http://cidrdb.org/cidr2021/papers/cidr2021_paper13.pdf). Wikipedia, which is often utilized as a rich knowledge source in general text settings, contains references to medical terms and can help address this issue. Here, we curate the MedWiki dataset, which is a large-scale, weakly-labeled dataset that consists of sentences from Wikipedia annotated with medical entities in the UMLS knowledge base. MedWiki can serve as a pretraining dataset for language models and holds potential for improving performance on medical named entity recognition tasks, especially on rare entities. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+MedWiki consists of sentences obtained from the November 2019 dump of English Wikipedia. We split pages into an 80/10/10 train/dev/test split and then segment each page at the sentence-level. This ensures that all sentences associated with a single Wikipedia page are placed in the same split. 
+
+#### Who are the source language producers?
+
+The source language producers are editors on English Wikipedia. 
+
+### Annotations
+
+#### Annotation process
+
+We create two configurations of our dataset: MedWiki-Full and MedWiki-HQ. We label MedWiki-Full by first annotating all English Wikipedia articles with textual mentions and corresponding WikiData entities; we do so by obtaining gold entity labels from internal page links as well as generating weak labels based on pronouns and alternative entity names (see [Orr et al. 2020](http://cidrdb.org/cidr2021/papers/cidr2021_paper13.pdf) for additional information).  Then, we use the off-the-shelf entity linker [Bootleg](https://github.com/HazyResearch/bootleg) to map entities in WikiData to their counterparts in the 2017AA release of the Unified Medical Language System (UMLS), a standard knowledge base for biomedical entities (additional implementation details in forthcoming publication). Any sentence containing at least one UMLS entity is included in MedWiki-Full. We also include types associated with each entity, which are collected from both WikiData and UMLS using the generated UMLS-Wikidata mapping. It is important to note that types obtained from WikiData are filtered according to methods described in [Orr et al. 2020](http://cidrdb.org/cidr2021/papers/cidr2021_paper13.pdf). 
+
+Since our labeling procedure introduces some noise into annotations, we also release the MedWiki-HQ dataset configuration with higher-quality labels. To generate MedWiki-HQ, we filtered the UMLS-Wikidata mappings to only include pairs of UMLS medical entities and WikiData items that share a high textual overlap between titles. MedWiki-HQ is a subset of MedWiki-Full.
+
+To evaluate the quality of our UMLS-Wikidata mappings, we find that WikiData includes a small set of "true" labeled mappings between UMLS entities and WikiData items. (Note that we only include WikiData items associated with linked Wikipedia pages.) This set comprises approximately 9.3k UMLS entities in the original UMLS-Wikidata mapping (used for MedWiki-Full) and 5.6k entities in the filtered UMLS-Wikidata mapping (used for MedWiki-HQ). Using these labeled sets, we find that our mapping accuracy is 80.2% for the original UMLS-Wikidata mapping and 94.5% for the filtered UMLS-Wikidata mapping. We also evaluate integration performance on this segment as the proportion of mapped WikiData entities that share a WikiData type with the true entity, suggesting the predicted mapping adds relevant structural resources. Integration performance is 85.4% for the original UMLS-Wikidata mapping and 95.9% for the filtered UMLS-Wikidata mapping. The remainder of items in UMLS have no “true” mappings to WikiData.
+
+#### Who are the annotators?
+
+The dataset was labeled using weak-labeling techniques as described above.
+
+### Personal and Sensitive Information
+
+No personal or sensitive information is included in MedWiki.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The purpose of this dataset is to enable the creation of better named entity recognition systems for biomedical text. MedWiki encompasses a large set of entities in the UMLS knowledge base and includes a rich set of types associated with each entity, which can enable the creation of models that achieve high performance on named entity recognition tasks, especially on rare or unpopular entities. Such systems hold potential for improving automated parsing and information retrieval from large quantities of biomedical text.
+
+### Discussion of Biases
+
+The data included in MedWiki comes from English Wikipedia. Generally, Wikipedia articles are neutral in point of view and aim to avoid bias. However, some [prior work](https://www.hbs.edu/ris/Publication%20Files/15-023_e044cf50-f621-4759-a827-e9a3bf8920c0.pdf) has shown that ideological biases may exist within some Wikipedia articles, especially those that are focused on political issues or those that are written by fewer authors. We anticipate that such biases are rare for medical articles, which are typically comprised of scientific facts. However, it is important to note that bias encoded in Wikipedia is likely to be reflected by MedWiki.
+
+### Other Known Limitations
+
+Since MedWiki was annotated using weak labeling techniques, there is likely some noise in entity annotations. (Note that to address this, we include the MedWiki-HQ configuration, which is a subset of MedWiki-Full with higher quality labels. Additional details in [Dataset Creation](#dataset-creation)). 
+
+## Additional Information
+
+### Dataset Curators
+
+MedWiki was curated by Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, and Chris Ré. 
+
+### Licensing Information
+
+Dataset licensed under CC BY 4.0.
+
+### Citation Information
+
+```
+@inproceedings{varma-etal-2021-cross-domain,
+    title = "Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text",
+    author = "Varma, Maya  and
+      Orr, Laurel  and
+      Wu, Sen  and
+      Leszczynski, Megan  and
+      Ling, Xiao  and
+      R{\'e}, Christopher",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
+    month = nov,
+    year = "2021",
+    address = "Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-emnlp.388",
+    pages = "4566--4575",
+}
+```
+
+### Contributions
+
+Thanks to [@maya124](https://github.com/maya124) for adding this dataset.

huggingface_dataset/Dataset_Card/opentargets_clinical_trial_reason_to_stop.md

@@ -0,0 +1,174 @@
+---
+annotations_creators:
+- expert-generated
+language:
+- en
+language_creators:
+- expert-generated
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+pretty_name: clinical_trial_reason_to_stop
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- bio
+- research papers
+- clinical trial
+- drug development
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+- multi-label-classification
+---
+
+# Dataset Card for Clinical Trials's Reason to Stop
+
+## 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.opentargets.org
+- **Repository:** https://github.com/LesyaR/stopReasons
+- **Paper:**
+- **Point of Contact:** data@opentargets.org
+
+### Dataset Summary
+
+This dataset contains a curated classification of more than 5000 reasons why a clinical trial has suffered an early stop.
+The text has been extracted from clinicaltrials.gov, the largest resource of clinical trial information. The text has been curated by members of the Open Targets organisation, a project aimed at providing data relevant to drug development.
+
+All 17 possible classes have been carefully defined:
+- Business_Administrative
+- Another_Study
+- Negative
+- Study_Design
+- Invalid_Reason
+- Ethical_Reason
+- Insufficient_Data
+- Insufficient_Enrollment
+- Study_Staff_Moved
+- Endpoint_Met
+- Regulatory
+- Logistics_Resources
+- Safety_Sideeffects
+- No_Context
+- Success
+- Interim_Analysis
+- Covid19
+
+### Supported Tasks and Leaderboards
+
+Multi class classification
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+```json
+{'text': 'Due to company decision to focus resources on a larger, controlled study in this patient population."',
+ 'label': 'Another_Study'}
+```
+
+
+### Data Fields
+
+`text`: contains the reason for the CT early stop
+`label`: contains one of the 17 defined classes 
+
+### 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
+
+This dataset has an Apache 2.0 license.
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@ireneisdoomed](https://github.com/<github-username>) for adding this dataset.

huggingface_dataset/Dataset_Card/wikipedia.md

@@ -0,0 +1,956 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- crowdsourced
+pretty_name: Wikipedia
+paperswithcode_id: null
+license:
+- cc-by-sa-3.0
+- gfdl
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- language-modeling
+- masked-language-modeling
+source_datasets:
+- original
+multilinguality:
+- multilingual
+size_categories:
+- n<1K
+- 1K<n<10K
+- 10K<n<100K
+- 100K<n<1M
+- 1M<n<10M
+language:
+- aa
+- ab
+- ace
+- af
+- ak
+- als
+- am
+- an
+- ang
+- ar
+- arc
+- arz
+- as
+- ast
+- atj
+- av
+- ay
+- az
+- azb
+- ba
+- bar
+- bcl
+- be
+- bg
+- bh
+- bi
+- bjn
+- bm
+- bn
+- bo
+- bpy
+- br
+- bs
+- bug
+- bxr
+- ca
+- cbk
+- cdo
+- ce
+- ceb
+- ch
+- cho
+- chr
+- chy
+- ckb
+- co
+- cr
+- crh
+- cs
+- csb
+- cu
+- cv
+- cy
+- da
+- de
+- din
+- diq
+- dsb
+- dty
+- dv
+- dz
+- ee
+- el
+- eml
+- en
+- eo
+- es
+- et
+- eu
+- ext
+- fa
+- ff
+- fi
+- fj
+- fo
+- fr
+- frp
+- frr
+- fur
+- fy
+- ga
+- gag
+- gan
+- gd
+- gl
+- glk
+- gn
+- gom
+- gor
+- got
+- gu
+- gv
+- ha
+- hak
+- haw
+- he
+- hi
+- hif
+- ho
+- hr
+- hsb
+- ht
+- hu
+- hy
+- ia
+- id
+- ie
+- ig
+- ii
+- ik
+- ilo
+- inh
+- io
+- is
+- it
+- iu
+- ja
+- jam
+- jbo
+- jv
+- ka
+- kaa
+- kab
+- kbd
+- kbp
+- kg
+- ki
+- kj
+- kk
+- kl
+- km
+- kn
+- ko
+- koi
+- krc
+- ks
+- ksh
+- ku
+- kv
+- kw
+- ky
+- la
+- lad
+- lb
+- lbe
+- lez
+- lfn
+- lg
+- li
+- lij
+- lmo
+- ln
+- lo
+- lrc
+- lt
+- ltg
+- lv
+- lzh
+- mai
+- mdf
+- mg
+- mh
+- mhr
+- mi
+- min
+- mk
+- ml
+- mn
+- mr
+- mrj
+- ms
+- mt
+- mus
+- mwl
+- my
+- myv
+- mzn
+- na
+- nah
+- nan
+- nap
+- nds
+- ne
+- new
+- ng
+- nl
+- nn
+- 'no'
+- nov
+- nrf
+- nso
+- nv
+- ny
+- oc
+- olo
+- om
+- or
+- os
+- pa
+- pag
+- pam
+- pap
+- pcd
+- pdc
+- pfl
+- pi
+- pih
+- pl
+- pms
+- pnb
+- pnt
+- ps
+- pt
+- qu
+- rm
+- rmy
+- rn
+- ro
+- ru
+- rue
+- rup
+- rw
+- sa
+- sah
+- sat
+- sc
+- scn
+- sco
+- sd
+- se
+- sg
+- sgs
+- sh
+- si
+- sk
+- sl
+- sm
+- sn
+- so
+- sq
+- sr
+- srn
+- ss
+- st
+- stq
+- su
+- sv
+- sw
+- szl
+- ta
+- tcy
+- tdt
+- te
+- tg
+- th
+- ti
+- tk
+- tl
+- tn
+- to
+- tpi
+- tr
+- ts
+- tt
+- tum
+- tw
+- ty
+- tyv
+- udm
+- ug
+- uk
+- ur
+- uz
+- ve
+- vec
+- vep
+- vi
+- vls
+- vo
+- vro
+- wa
+- war
+- wo
+- wuu
+- xal
+- xh
+- xmf
+- yi
+- yo
+- yue
+- za
+- zea
+- zh
+- zu
+language_bcp47:
+- nds-nl
+configs:
+- 20220301.aa
+- 20220301.ab
+- 20220301.ace
+- 20220301.ady
+- 20220301.af
+- 20220301.ak
+- 20220301.als
+- 20220301.am
+- 20220301.an
+- 20220301.ang
+- 20220301.ar
+- 20220301.arc
+- 20220301.arz
+- 20220301.as
+- 20220301.ast
+- 20220301.atj
+- 20220301.av
+- 20220301.ay
+- 20220301.az
+- 20220301.azb
+- 20220301.ba
+- 20220301.bar
+- 20220301.bat-smg
+- 20220301.bcl
+- 20220301.be
+- 20220301.be-x-old
+- 20220301.bg
+- 20220301.bh
+- 20220301.bi
+- 20220301.bjn
+- 20220301.bm
+- 20220301.bn
+- 20220301.bo
+- 20220301.bpy
+- 20220301.br
+- 20220301.bs
+- 20220301.bug
+- 20220301.bxr
+- 20220301.ca
+- 20220301.cbk-zam
+- 20220301.cdo
+- 20220301.ce
+- 20220301.ceb
+- 20220301.ch
+- 20220301.cho
+- 20220301.chr
+- 20220301.chy
+- 20220301.ckb
+- 20220301.co
+- 20220301.cr
+- 20220301.crh
+- 20220301.cs
+- 20220301.csb
+- 20220301.cu
+- 20220301.cv
+- 20220301.cy
+- 20220301.da
+- 20220301.de
+- 20220301.din
+- 20220301.diq
+- 20220301.dsb
+- 20220301.dty
+- 20220301.dv
+- 20220301.dz
+- 20220301.ee
+- 20220301.el
+- 20220301.eml
+- 20220301.en
+- 20220301.eo
+- 20220301.es
+- 20220301.et
+- 20220301.eu
+- 20220301.ext
+- 20220301.fa
+- 20220301.ff
+- 20220301.fi
+- 20220301.fiu-vro
+- 20220301.fj
+- 20220301.fo
+- 20220301.fr
+- 20220301.frp
+- 20220301.frr
+- 20220301.fur
+- 20220301.fy
+- 20220301.ga
+- 20220301.gag
+- 20220301.gan
+- 20220301.gd
+- 20220301.gl
+- 20220301.glk
+- 20220301.gn
+- 20220301.gom
+- 20220301.gor
+- 20220301.got
+- 20220301.gu
+- 20220301.gv
+- 20220301.ha
+- 20220301.hak
+- 20220301.haw
+- 20220301.he
+- 20220301.hi
+- 20220301.hif
+- 20220301.ho
+- 20220301.hr
+- 20220301.hsb
+- 20220301.ht
+- 20220301.hu
+- 20220301.hy
+- 20220301.ia
+- 20220301.id
+- 20220301.ie
+- 20220301.ig
+- 20220301.ii
+- 20220301.ik
+- 20220301.ilo
+- 20220301.inh
+- 20220301.io
+- 20220301.is
+- 20220301.it
+- 20220301.iu
+- 20220301.ja
+- 20220301.jam
+- 20220301.jbo
+- 20220301.jv
+- 20220301.ka
+- 20220301.kaa
+- 20220301.kab
+- 20220301.kbd
+- 20220301.kbp
+- 20220301.kg
+- 20220301.ki
+- 20220301.kj
+- 20220301.kk
+- 20220301.kl
+- 20220301.km
+- 20220301.kn
+- 20220301.ko
+- 20220301.koi
+- 20220301.krc
+- 20220301.ks
+- 20220301.ksh
+- 20220301.ku
+- 20220301.kv
+- 20220301.kw
+- 20220301.ky
+- 20220301.la
+- 20220301.lad
+- 20220301.lb
+- 20220301.lbe
+- 20220301.lez
+- 20220301.lfn
+- 20220301.lg
+- 20220301.li
+- 20220301.lij
+- 20220301.lmo
+- 20220301.ln
+- 20220301.lo
+- 20220301.lrc
+- 20220301.lt
+- 20220301.ltg
+- 20220301.lv
+- 20220301.mai
+- 20220301.map-bms
+- 20220301.mdf
+- 20220301.mg
+- 20220301.mh
+- 20220301.mhr
+- 20220301.mi
+- 20220301.min
+- 20220301.mk
+- 20220301.ml
+- 20220301.mn
+- 20220301.mr
+- 20220301.mrj
+- 20220301.ms
+- 20220301.mt
+- 20220301.mus
+- 20220301.mwl
+- 20220301.my
+- 20220301.myv
+- 20220301.mzn
+- 20220301.na
+- 20220301.nah
+- 20220301.nap
+- 20220301.nds
+- 20220301.nds-nl
+- 20220301.ne
+- 20220301.new
+- 20220301.ng
+- 20220301.nl
+- 20220301.nn
+- 20220301.no
+- 20220301.nov
+- 20220301.nrm
+- 20220301.nso
+- 20220301.nv
+- 20220301.ny
+- 20220301.oc
+- 20220301.olo
+- 20220301.om
+- 20220301.or
+- 20220301.os
+- 20220301.pa
+- 20220301.pag
+- 20220301.pam
+- 20220301.pap
+- 20220301.pcd
+- 20220301.pdc
+- 20220301.pfl
+- 20220301.pi
+- 20220301.pih
+- 20220301.pl
+- 20220301.pms
+- 20220301.pnb
+- 20220301.pnt
+- 20220301.ps
+- 20220301.pt
+- 20220301.qu
+- 20220301.rm
+- 20220301.rmy
+- 20220301.rn
+- 20220301.ro
+- 20220301.roa-rup
+- 20220301.roa-tara
+- 20220301.ru
+- 20220301.rue
+- 20220301.rw
+- 20220301.sa
+- 20220301.sah
+- 20220301.sat
+- 20220301.sc
+- 20220301.scn
+- 20220301.sco
+- 20220301.sd
+- 20220301.se
+- 20220301.sg
+- 20220301.sh
+- 20220301.si
+- 20220301.simple
+- 20220301.sk
+- 20220301.sl
+- 20220301.sm
+- 20220301.sn
+- 20220301.so
+- 20220301.sq
+- 20220301.sr
+- 20220301.srn
+- 20220301.ss
+- 20220301.st
+- 20220301.stq
+- 20220301.su
+- 20220301.sv
+- 20220301.sw
+- 20220301.szl
+- 20220301.ta
+- 20220301.tcy
+- 20220301.te
+- 20220301.tet
+- 20220301.tg
+- 20220301.th
+- 20220301.ti
+- 20220301.tk
+- 20220301.tl
+- 20220301.tn
+- 20220301.to
+- 20220301.tpi
+- 20220301.tr
+- 20220301.ts
+- 20220301.tt
+- 20220301.tum
+- 20220301.tw
+- 20220301.ty
+- 20220301.tyv
+- 20220301.udm
+- 20220301.ug
+- 20220301.uk
+- 20220301.ur
+- 20220301.uz
+- 20220301.ve
+- 20220301.vec
+- 20220301.vep
+- 20220301.vi
+- 20220301.vls
+- 20220301.vo
+- 20220301.wa
+- 20220301.war
+- 20220301.wo
+- 20220301.wuu
+- 20220301.xal
+- 20220301.xh
+- 20220301.xmf
+- 20220301.yi
+- 20220301.yo
+- 20220301.za
+- 20220301.zea
+- 20220301.zh
+- 20220301.zh-classical
+- 20220301.zh-min-nan
+- 20220301.zh-yue
+- 20220301.zu
+dataset_info:
+- config_name: 20220301.de
+  features:
+  - name: id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 8905282792
+    num_examples: 2665357
+  download_size: 6523215105
+  dataset_size: 8905282792
+- config_name: 20220301.en
+  features:
+  - name: id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 20275516160
+    num_examples: 6458670
+  download_size: 20598313936
+  dataset_size: 20275516160
+- config_name: 20220301.fr
+  features:
+  - name: id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 7375920768
+    num_examples: 2402095
+  download_size: 5602565274
+  dataset_size: 7375920768
+- config_name: 20220301.frr
+  features:
+  - name: id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 9129760
+    num_examples: 15199
+  download_size: 12438017
+  dataset_size: 9129760
+- config_name: 20220301.it
+  features:
+  - name: id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 4539944448
+    num_examples: 1743035
+  download_size: 3516441239
+  dataset_size: 4539944448
+- config_name: 20220301.simple
+  features:
+  - name: id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 235072360
+    num_examples: 205328
+  download_size: 239682796
+  dataset_size: 235072360
+---
+
+# Dataset Card for Wikipedia
+
+## 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://dumps.wikimedia.org](https://dumps.wikimedia.org)
+- **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)
+
+### Dataset Summary
+
+Wikipedia dataset containing cleaned articles of all languages.
+The datasets are built from the Wikipedia dump
+(https://dumps.wikimedia.org/) with one split per language. Each example
+contains the content of one full Wikipedia article with cleaning to strip
+markdown and unwanted sections (references, etc.).
+
+The articles are parsed using the ``mwparserfromhell`` tool.
+
+To load this dataset you need to install Apache Beam and ``mwparserfromhell`` first:
+
+```
+pip install apache_beam mwparserfromhell
+```
+
+Then, you can load any subset of Wikipedia per language and per date this way:
+
+```python
+from datasets import load_dataset
+
+load_dataset("wikipedia", language="sw", date="20220120", beam_runner=...)
+```
+where you can pass as `beam_runner` any Apache Beam supported runner for (distributed) data processing 
+(see [here](https://beam.apache.org/documentation/runners/capability-matrix/)).
+Pass "DirectRunner" to run it on your machine. 
+
+You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).
+
+Some subsets of Wikipedia have already been processed by HuggingFace, and you can load them just with:
+```python
+from datasets import load_dataset
+
+load_dataset("wikipedia", "20220301.en")
+```
+
+The list of pre-processed subsets is:
+- "20220301.de"
+- "20220301.en"
+- "20220301.fr"
+- "20220301.frr"
+- "20220301.it"
+- "20220301.simple"
+
+### Supported Tasks and Leaderboards
+
+The dataset is generally used for Language Modeling.
+
+### Languages
+
+You can find the list of languages [here](https://meta.wikimedia.org/wiki/List_of_Wikipedias).
+
+## Dataset Structure
+
+### Data Instances
+
+An example looks as follows:
+
+```
+{'id': '1',
+ 'url': 'https://simple.wikipedia.org/wiki/April',
+ 'title': 'April',
+ 'text': 'April is the fourth month...'
+}
+```
+
+Some subsets of Wikipedia have already been processed by HuggingFace, as you can see below:
+
+#### 20220301.de
+
+- **Size of downloaded dataset files:** 6523.22 MB
+- **Size of the generated dataset:** 8905.28 MB
+- **Total amount of disk used:** 15428.50 MB
+
+#### 20220301.en
+
+- **Size of downloaded dataset files:** 20598.31 MB
+- **Size of the generated dataset:** 20275.52 MB
+- **Total amount of disk used:** 40873.83 MB
+
+#### 20220301.fr
+
+- **Size of downloaded dataset files:** 5602.57 MB
+- **Size of the generated dataset:** 7375.92 MB
+- **Total amount of disk used:** 12978.49 MB
+
+#### 20220301.frr
+
+- **Size of downloaded dataset files:** 12.44 MB
+- **Size of the generated dataset:** 9.13 MB
+- **Total amount of disk used:** 21.57 MB
+
+#### 20220301.it
+
+- **Size of downloaded dataset files:** 3516.44 MB
+- **Size of the generated dataset:** 4539.94 MB
+- **Total amount of disk used:** 8056.39 MB
+
+#### 20220301.simple
+
+- **Size of downloaded dataset files:** 239.68 MB
+- **Size of the generated dataset:** 235.07 MB
+- **Total amount of disk used:** 474.76 MB
+
+### Data Fields
+
+The data fields are the same among all configurations:
+
+- `id` (`str`): ID of the article.
+- `url` (`str`): URL of the article.
+- `title` (`str`): Title of the article.
+- `text` (`str`): Text content of the article.
+
+### Data Splits
+
+Here are the number of examples for several configurations:
+
+| name            |   train |
+|-----------------|--------:|
+| 20220301.de     | 2665357 |
+| 20220301.en     | 6458670 |
+| 20220301.fr     | 2402095 |
+| 20220301.frr    |   15199 |
+| 20220301.it     | 1743035 |
+| 20220301.simple |  205328 |
+
+## 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
+
+Most of Wikipedia's text and many of its images are co-licensed under the
+[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
+(CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License)
+(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). 
+
+Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such
+text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes
+the text.
+
+### Citation Information
+
+```
+@ONLINE{wikidump,
+    author = "Wikimedia Foundation",
+    title  = "Wikimedia Downloads",
+    url    = "https://dumps.wikimedia.org"
+}
+```
+
+### Contributions
+
+Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

huggingface_dataset/Dataset_Card/zpn_tox21_srp53.md

@@ -0,0 +1,134 @@
+---
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: tox21_srp53
+size_categories:
+- 1K<n<10K
+source_datasets: []
+tags:
+- bio
+- bio-chem
+- molnet
+- molecule-net
+- biophysics
+task_categories:
+- other
+task_ids: []
+dataset_info:
+  features:
+  - name: smiles
+    dtype: string
+  - name: selfies
+    dtype: string
+  - name: target
+    dtype:
+      class_label:
+        names:
+          '0': '0'
+          '1': '1'
+  splits:
+  - name: train
+    num_bytes: 1055437
+    num_examples: 6264
+  - name: test
+    num_bytes: 223704
+    num_examples: 784
+  - name: validation
+    num_bytes: 224047
+    num_examples: 783
+  download_size: 451728
+  dataset_size: 1503188
+---
+
+# Dataset Card for tox21_srp53
+
+## 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://moleculenet.org/**
+- **Repository: https://github.com/deepchem/deepchem/tree/master**
+- **Paper: https://arxiv.org/abs/1703.00564**
+
+### Dataset Summary
+
+`tox21_srp53` is a dataset included in [MoleculeNet](https://moleculenet.org/). It is the p53 stress-response pathway activation (SR-p53) task from Tox21.
+
+## Dataset Structure
+
+### Data Fields
+
+Each split contains
+
+* `smiles`: the [SMILES](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system) representation of a molecule
+* `selfies`: the [SELFIES](https://github.com/aspuru-guzik-group/selfies) representation of a molecule
+* `target`: clinical trial toxicity (or absence of toxicity) 
+
+### Data Splits
+
+The dataset is split into an 80/10/10 train/valid/test split using scaffold split. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was originially generated by the Pande Group at Standford
+
+### Licensing Information
+
+This dataset was originally released under an MIT license
+
+### Citation Information
+
+```
+@misc{https://doi.org/10.48550/arxiv.1703.00564,
+  doi = {10.48550/ARXIV.1703.00564},
+  
+  url = {https://arxiv.org/abs/1703.00564},
+  
+  author = {Wu, Zhenqin and Ramsundar, Bharath and Feinberg, Evan N. and Gomes, Joseph and Geniesse, Caleb and Pappu, Aneesh S. and Leswing, Karl and Pande, Vijay},
+  
+  keywords = {Machine Learning (cs.LG), Chemical Physics (physics.chem-ph), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
+  
+  title = {MoleculeNet: A Benchmark for Molecular Machine Learning},
+  
+  publisher = {arXiv},
+  
+  year = {2017},
+  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```
+
+### Contributions
+
+Thanks to [@zanussbaum](https://github.com/zanussbaum) for adding this dataset.