Upload batch 355 (20 files, last=huggingface_dataset/Dataset_Card/huggingartists_sektor-gaza.md)

huggingface_dataset/Dataset_Card/LRGB_coco_superpixels_edge_wt_only_feat_30.md

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+---
+task_categories:
+- graph-ml
+size_categories:
+- 1M<n<10M
+tags:
+- lrgb
+license: cc-by-4.0
+---
+
+# `coco_superpixels_edge_wt_only_feat_30`
+
+### Dataset Summary
+
+|  Dataset | Domain  |  Task | Node Feat. (dim)  | Edge Feat. (dim) | Perf. Metric | 
+|---|---|---|---|---|---|
+| COCO-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 
+|---|---:|---:|---:|:---:|---:|---:|---:|---:|
+| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
+
+## Additional Information
+
+### Dataset Curators
+
+* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
+
+### 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/Nerfgun3_sam_yang.md

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+---
+language:
+- en
+tags:
+- stable-diffusion
+- text-to-image
+license: creativeml-openrail-m
+inference: false
+
+---
+
+# Sam Yang Artist Embedding / Textual Inversion
+
+## Usage
+To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
+
+To use it in a prompt: ```"drawn by sam_yang"```
+
+If it is to strong just add [] around it.
+
+Trained until 5000 steps
+
+Have fun :)
+
+## Example Pictures
+
+<table>
+  <tr>
+    <td><img src=https://i.imgur.com/cbtBjwH.png width=100% height=100%/></td>
+    <td><img src=https://i.imgur.com/r5s8bSO.png width=100% height=100%/></td>
+    <td><img src=https://i.imgur.com/NpGj5KU.png width=100% height=100%/></td>
+    <td><img src=https://i.imgur.com/eWJlaf5.png width=100% height=100%/></td>
+    <td><img src=https://i.imgur.com/DOJvxTJ.png width=100% height=100%/></td>
+   </tr>
+</table>
+
+## License
+
+This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
+The CreativeML OpenRAIL License specifies: 
+
+1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 
+2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
+3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
+[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

huggingface_dataset/Dataset_Card/Pacoch_postglacial-shaded-relief.md

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+---
+license: mit
+task_categories:
+- image-classification
+- feature-extraction
+tags:
+- geomorphology
+- image
+- png
+pretty_name: >-
+  Shaded relief image dataset for geomorphological studies of Polish postglacial
+  landscape
+size_categories:
+- 1M<n<10M
+---
+
+## Shaded relief image dataset for geomorphological studies of Polish postglacial landscape
+
+This dataset contains a list of 138 png images of shaded relief cut into the 128x128 arrays. The area that the dataset covers is compacted within the
+two main geomorphological spheres in Poland - postglacial denuded and nondenuded landscape. Arrays representing one of two categories are labeled accordingly.
+
+Shaded relief scene has been calculated with exposition and sunlight paramiters set to direct south (thus, in this case - 180 degrees).

huggingface_dataset/Dataset_Card/acul3_KoPI-CC_News.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- id
+license: cc
+source_datasets:
+- original
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+paperswithcode_id: oscar
+---
+
+### Dataset Summary
+
+KoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter  using fasttext
+
+detail soon
+
+
+
+```
+
+

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-alex-apostolo__filtered-cuad-alex-apostolo__filtered-cu-fd7768-3096988008.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- alex-apostolo/filtered-cuad
+eval_info:
+  task: extractive_question_answering
+  model: alex-apostolo/legal-roberta-base-filtered-cuad
+  metrics: ['accuracy']
+  dataset_name: alex-apostolo/filtered-cuad
+  dataset_config: alex-apostolo--filtered-cuad
+  dataset_split: test
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: alex-apostolo/legal-roberta-base-filtered-cuad
+* Dataset: alex-apostolo/filtered-cuad
+* Config: alex-apostolo--filtered-cuad
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@pankajm](https://huggingface.co/pankajm) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__exampletx-constructive-7f6ba0-1708559813.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- phpthinh/exampletx
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-1b1
+  metrics: []
+  dataset_name: phpthinh/exampletx
+  dataset_config: constructive
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: bigscience/bloom-1b1
+* Dataset: phpthinh/exampletx
+* Config: constructive
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-lener_br-lener_br-f0f34b-15626154.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- lener_br
+eval_info:
+  task: entity_extraction
+  model: Luciano/bertimbau-base-lener-br-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/bertimbau-base-lener-br-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 [@https://huggingface.co/Luciano/bertimbau-base-lener-br-finetuned-lener-br](https://huggingface.co/https://huggingface.co/Luciano/bertimbau-base-lener-br-finetuned-lener-br) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-billsum-a6bd4aa5-11965601.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- billsum
+eval_info:
+  task: summarization
+  model: pszemraj/long-t5-tglobal-base-16384-book-summary
+  metrics: []
+  dataset_name: billsum
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: text
+    target: summary
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: pszemraj/long-t5-tglobal-base-16384-book-summary
+* Dataset: billsum
+* Config: default
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

huggingface_dataset/Dataset_Card/biglam_atypical_animacy.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+language_creators:
+- machine-generated
+license:
+- cc0-1.0
+multilinguality:
+- monolingual
+paperswithcode_id: null
+pretty_name: Atypical Animacy
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+- intent-classification
+---
+# Dataset Card for atypical_animacy
+
+## 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://bl.iro.bl.uk/concern/datasets/323177af-6081-4e93-8aaf-7932ca4a390a?locale=en
+- **Repository:** https://github.com/Living-with-machines/AtypicalAnimacy
+- **Paper:** https://arxiv.org/abs/2005.11140
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Mariona Coll Ardanuy](mailto:mcollardanuy@turing.ac.uk), [Daniel CS Wilson](mailto:dwilson@turing.ac.uk)
+
+### Dataset Summary
+
+Atypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library. This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence. 
+
+### Supported Tasks and Leaderboards
+
+- `text-classification` - This dataset can be used to determine if a mention of an entity in a document was humanlike or not
+- `entity-recognition` - The dataset can be used to fine tune large models for NER, albeit for a very specific use case
+
+### Languages
+
+The text in the dataset is in English, as written by authors of books digitized by the British Library. The associated BCP-47 code in `en`
+
+## Dataset Structure
+
+The dataset has a single configuration
+
+### Data Instances
+
+An example data point
+
+```
+{'id': '002757962_01_184_16', 
+'sentence': '100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue.', 
+'context': 'Fig.  100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue.  The effect of this on a long boiler is to cause springing and leakage of the seams from the heat being applied to one side of the boiler only.', 
+'target': 'boiler', 
+'animacy': 0.0, 
+'humanness': 1.0, 
+'offsets': [20, 26], 
+'date': '1893'}
+```
+
+### Data Fields
+
+- id: sentence identifier according to internal Living with Machines BL books indexing.
+- sentence: sentence where target expression occurs.
+- context: sentence where target expression occurs, plus one sentence to the left and one sentence to the right.
+- target: target expression
+- animacy: animacy of the target expression
+- humanness: humanness of the target expression
+
+
+### Data Splits
+
+Train | 598
+
+## Dataset Creation
+
+The dataset was created by manually annotating books that had been digitized by the British Library. According to the paper's authors, 
+> "we provide a basis for examining how machines were imagined during the nineteenth century as everything from lifeless mechanical objects to living beings, or even human-like agents that feel, think, and love. We focus on texts from nineteenth-century Britain, a society being transformed by industrialization, as a good candidate for studying the broader issue"
+
+### Curation Rationale
+
+From the paper: 
+> The Stories dataset is largely composed of target expressions that correspond to either typically animate or typically inanimate entities. Even though some cases of unconventional animacy can be found(folktales, in particular, are richer in typically inanimate entities that become animate), these accountfor a very small proportion of the data.6 We decided to create our own dataset (henceforth 19thC Machines dataset) to gain a better sense of the suitability of our method to the problem of atypical animacy detection, with particular attention to the case of animacy of machines in nineteenth-century texts.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset was generated by manually annotating books that have been digitized by the British Library
+
+#### Who are the source language producers?
+
+The data was originally produced by British authors in the 19th century. The books were then digitzed whcih produces some noise due to the OCR method. The annotators are from The Alan Turing Institute, The British Library, University of Cambridge, University of Exeter and Queen Mary University of London
+
+### Annotations
+
+#### Annotation process
+Annotation was carried out in two parts. 
+For the intial annotation process, from the paper:
+> "For human annotators, even history and literature experts, language subtleties made this task extremely subjective. In the first task, we masked the target word (i.e. the machine) in each sentence and asked the annotator to fill the slot with the most likely entity between ‘human’, ‘horse’, and ‘machine’, representing three levels in the animacy hierarchy: human, animal, and object (Comrie, 1989, 185). We asked annotators to stick to the most literal meaning and avoid metaphorical interpretations when possible. The second task was more straightforwardly related to determining the animacy of the target entity, given the same 100 sentences. We asked annotators to provide a score between -2 and 2, with -2 being definitely inanimate, -1 possibly inanimate, 1 possibly animate, and 2 definitely animate. Neutral judgements were not allowed. "
+
+For the final annotations, from the paper:
+> A subgroup of five annotators collaboratively wrote the guidelines based on their experience annotating the first batch of sentences, taking into account common discrepancies. After discussion, it was decided that a machine would be tagged as animate if it is described as having traits distinctive of biologically animate beings or human-specific skills, or portrayed as having feelings, emotions, or a soul. Sentences like the ones in example 2 would be considered animate, but an additional annotation layer would be provided to capture the notion of humanness, which would be true if the machine is portrayed as sentient and capable of specifically human emotions, and false if it used to suggest some degree of dehumanization.
+
+#### Who are the annotators?
+ Annotations were carried out by the following people 
+- Giorgia Tolfo
+- Ruth Ahnert
+- Kaspar Beelen
+- Mariona Coll Ardanuy
+- Jon Lawrence
+- Katherine McDonough
+- Federico Nanni
+- Daniel CS Wilson
+
+### Personal and Sensitive Information
+
+This dataset does not have any personal information since they are digitizations of books from the 19th century. Some passages might be sensitive, but it is not explicitly mentioned in the paper.
+
+## 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
+
+The curators for this dataset are:
+- Kaspar Beelen
+- Mariona Coll Ardanuy
+- Federico Nanni
+- Giorgia Tolfo
+
+
+### Licensing Information
+CC0 1.0 Universal Public Domain
+
+### Citation Information
+
+```
+@article{DBLP:journals/corr/abs-2005-11140,
+  author    = {Mariona Coll Ardanuy and
+               Federico Nanni and
+               Kaspar Beelen and
+               Kasra Hosseini and
+               Ruth Ahnert and
+               Jon Lawrence and
+               Katherine McDonough and
+               Giorgia Tolfo and
+               Daniel C. S. Wilson and
+               Barbara McGillivray},
+  title     = {Living Machines: {A} study of atypical animacy},
+  journal   = {CoRR},
+  volume    = {abs/2005.11140},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2005.11140},
+  eprinttype = {arXiv},
+  eprint    = {2005.11140},
+  timestamp = {Sat, 23 Jan 2021 01:12:25 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2005-11140.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

huggingface_dataset/Dataset_Card/eli5_category.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+paperswithcode_id: null
+pretty_name: ELI5-Category
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|eli5
+task_categories:
+- text2text-generation
+task_ids:
+- abstractive-qa
+- open-domain-abstractive-qa
+dataset_info:
+  features:
+  - name: q_id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: selftext
+    dtype: string
+  - name: category
+    dtype: string
+  - name: subreddit
+    dtype: string
+  - name: answers
+    struct:
+    - name: a_id
+      sequence: string
+    - name: text
+      sequence: string
+    - name: score
+      sequence: int32
+    - name: text_urls
+      sequence:
+        sequence: string
+  - name: title_urls
+    sequence: string
+  - name: selftext_urls
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 166409797
+    num_examples: 91772
+  - name: validation1
+    num_bytes: 13150585
+    num_examples: 5446
+  - name: validation2
+    num_bytes: 4737744
+    num_examples: 2375
+  - name: test
+    num_bytes: 10419098
+    num_examples: 5411
+  download_size: 72921829
+  dataset_size: 194717224
+---
+
+# Dataset Card for ELI5-Category
+
+## 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:** [ELI5-Category homepage](https://celeritasml.netlify.app/posts/2021-12-01-eli5c/)
+- **Repository:** [ELI5-Category repository](https://github.com/rexarski/ANLY580-final-project)
+- **Point of Contact:** [Jingsong Gao](mailto:jg2109@georgetown.edu)
+
+### Dataset Summary
+
+The ELI5-Category dataset is a smaller but newer and categorized version of the original ELI5 dataset. It's an English-language dataset of questions and answers gathered from the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subreddit where users ask factual questions requiring paragraph-length or longer answers. After 2017, a tagging system was introduced to this subreddit so that the questions can be categorized into different topics according to their tags. Since the training and validation set is built by questions in different topics, the dataset is expected to alleviate the train/validation overlapping issue in the original [ELI5 dataset](https://huggingface.co/datasets/eli5).
+
+### Supported Tasks and Leaderboards
+
+- `abstractive-qa`, `open-domain-abstractive-qa`: The dataset can be used to train a model for Open Domain Long Form Question Answering. An LFQA model is presented with a non-factoid and asked to retrieve relevant information from a knowledge source (such as [Wikipedia](https://www.wikipedia.org/)), then use it to generate a multi-sentence answer. 
+
+### Languages
+
+The text in the dataset is in English, as spoken by Reddit users on the  [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subreddit. The associated BCP-47 code is `en`.
+
+## Dataset Structure
+
+### Data Instances
+
+The structure of this dataset is very similar to the original [ELI5 dataset](https://huggingface.co/datasets/eli5). A typical data point comprises a question, with a `title` containing the main question and a `selftext` which sometimes elaborates on it, and a list of answers from the forum sorted by scores they obtained. Additionally, the URLs in each of the text fields have been extracted to respective lists and replaced by generic tokens in the text.   
+In addition to the original ELI5 dataset, the data point also has a `category` field. There are 11 common values of `category` in this dataset: `Biology`,`Chemistry`,`Culture`,`Earth Science`,`Economics`,`Engineering`,`Mathematics`,`Other`,`Physics`,`Psychology`,`Technology`, and a special `category`: `Repost` indicates the same question has been asked before.
+ 
+An example from the ELI5-Category set looks as follows:
+```
+{'q_id': '5lcm18',
+ 'title': 'Why do old games running on new hardware still have technical issues ?',
+ 'selftext': 'I am playing some mega man games on my Xbox One and experience slowdown when there are a lot of enemies on screen . but the Xbox One is significantly more powerful than the NES , so why is there still slowdown on this hardware ?',
+ 'category': 'Engineering',
+ 'subreddit': 'explainlikeimfive',
+ 'answers': {'a_id': ['dbuo48e', 'dbusfve'],
+  'text': ["The XBox is emulating NES hardware and running the emulation at a set speed . If it ran it at as fast as possible , then it would be several times faster than the original NES game and would be unplayable . I ca n't speak for Mega Man exactly , but older games tended to run on a cycle locked to the screen refresh which was a fixed 60Hz or 50Hz . There was only one piece of hardware they ran on , so there was no need to adjust for different hardware speeds .",
+            "In that case , it 's probably on purpose - they want to emulate the experience as closely as possible , even including the slowdown and sprite flickering . Some emulators let you turn it off , but it 's usually turned on by default . In other cases , like if you 're trying to emulate PS2 games on your PC , the game might just run really slow in general . Even though your PC is way more powerful than a PS2 , it has to \" translate \" from PS2 language to PC language in realtime , which is much more difficult than running PS2 code on the PS2 itself ."],
+  'score': [13, 3],
+  'text_urls': [[],[]]},
+ 'title_urls': {'url': []},
+ 'selftext_urls': {'url': []}}
+```
+
+### Data Fields
+
+- `q_id`: a string question identifier for each example, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/submissions/) Reddit submission dumps
+- `subreddit`: always `explainlikeimfive`, indicating which subreddit the question came from
+- `category`: tag of the question, the possible values are listed above.
+- `title`: title of the question, with URLs extracted and replaced by `URL_n` tokens
+- `title_urls`: list of the extracted URLs, the `n`th element of the list was replaced by `URL_n`
+- `selftext`: either an empty string or an elaboration of the question
+- `selftext_urls`: similar to `title_urls` but for `self_text`
+- `answers`: a list of answers, each answer has:
+  - `a_id`: a string answer identifier for each answer, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/comments/) Reddit comments dumps.
+  - `text`: the answer text with the URLs normalized
+  - `score`: the number of upvotes - the number of downvotes the answer had received when the dumps were created
+  - `text_urls`: lists of the extracted URLs for every answer
+
+### Data Splits
+
+In order to avoid having duplicate questions across sets, three non-overlapping subsets of `category` are used in the training, validation and test set. Also, a special validation set contains all the questions in the `Repost` category. A valid retriever-generator model should have consistent performances on both validation sets.  
+The final split sizes are as follows:
+
+|                   | Train   | Valid | Valid2 |Test |
+| -----             | ------ | ----- | ---- | ---- |
+| `Biology`         | 32769 |       |       |      |
+| `Chemistry`       | 6633  |       |       |      |
+| `Culture`         |       | 5446  |       |      |
+| `Earth Science`   | 677   |       |       |      |
+| `Economics`       | 5901  |       |       |      |
+| `Engineering`     |       |       |       | 5411 |
+| `Mathematics`     | 1912  |       |       |      |
+| `Other`           | 19312 |       |       |      |
+| `Physics`         | 10196 |       |       |      |
+| `Psychology`      | 338   |       |       |      |
+| `Technology`      | 14034 |       |       |      |
+| `Repost`          |       |       | 2375  |      |
+| **Total**         | 91772 | 5446  | 2375  | 5411 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+ELI5-Category was built to provide a testbed for machines to learn how to answer more complex questions, which requires them to find and combine the information in a coherent manner. The dataset was built by gathering questions that were asked by community members of three subreddits, including [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), along with the answers that were provided by other users. The [rules of the subreddit](https://www.reddit.com/r/explainlikeimfive/wiki/detailed_rules) make this data particularly well suited to training a model for abstractive question answering: the questions need to seek an objective explanation about well-established facts, and the answers provided need to be understandable to a layperson without any particular knowledge domain.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data was obtained by filtering submissions and comments from the subreddits of interest from the XML dumps of the [Reddit forum](https://www.reddit.com/) hosted on [Pushshift.io](https://files.pushshift.io/reddit/).
+
+In order to further improve the quality of the selected examples, only questions with a score of at least 2 and at least one answer with a score of at least 2 were selected for the dataset. The dataset questions and answers span a period from January 2017 to June 2021.
+
+#### Who are the source language producers?
+
+The language producers are users of the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subreddit between 2017 and 2021. No further demographic information was available from the data source.
+
+### Annotations
+
+The dataset contains the `category` as an additional annotation for the topics of questions.
+
+#### Annotation process
+
+The dataset is auto-annotated by the tags of posts in the [Reddit forum](https://www.reddit.com/). 
+
+#### Who are the annotators?
+
+The annotators are users/administrators of the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subreddit between 2017 and 2021. No further demographic information was available from the data source.
+
+### Personal and Sensitive Information
+
+The authors removed the speaker IDs from the [Pushshift.io](https://files.pushshift.io/reddit/) dumps but did not otherwise anonymize the data. Some questions and answers are about contemporary public figures or individuals who appeared in the news.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The dataset has a similar social impact to the original ELI5 dataset [Social Impact of Dataset](https://huggingface.co/datasets/eli5#social-impact-of-dataset).
+
+### Discussion of Biases
+
+The dataset has similar considerations of biases to the original ELI5 dataset [Discussion of Biases](https://huggingface.co/datasets/eli5#discussion-of-biases).
+
+### Other Known Limitations
+
+The dataset has similar limitations to the original ELI5 dataset [Other Known Limitations](https://huggingface.co/datasets/eli5#other-known-limitations).
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was initially created by Jingsong Gao, Qinren Zhou, Rui Qiu, during a course project of `ANLY 580`: NLP for Data Analytics at Georgetown University.
+
+### Licensing Information
+
+The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear.
+
+### Citation Information
+
+```
+@inproceedings{eli5-category,
+  author    = {Jingsong Gao and
+               Qingren Zhou and
+               Rui Qiu},
+  title     = {{ELI5-Category:} A categorized open-domain QA dataset},
+  year      = {2021}
+}
+```
+
+### Contributions
+
+Thanks to [@jingshenSN2](https://github.com/jingshenSN2), [@QinrenZhou](https://github.com/QinrenZhou), [@rexarski](https://github.com/rexarski) for adding this dataset.

huggingface_dataset/Dataset_Card/fake_news_english.md

@@ -0,0 +1,188 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-label-classification
+pretty_name: Fake News English
+dataset_info:
+  features:
+  - name: article_number
+    dtype: int32
+  - name: url_of_article
+    dtype: string
+  - name: fake_or_satire
+    dtype:
+      class_label:
+        names:
+          '0': Satire
+          '1': Fake
+  - name: url_of_rebutting_article
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 78078
+    num_examples: 492
+  download_size: 3002233
+  dataset_size: 78078
+---
+
+# Dataset Card for Fake News English
+
+## 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://dl.acm.org/doi/10.1145/3201064.3201100**
+- **Repository:** https://github.com/jgolbeck/fakenews/
+- **Paper:** https://doi.org/10.1145/3201064.3201100
+- **Leaderboard:**
+- **Point of Contact:** Jennifer Golbeck (http://www.jengolbeck.com)
+
+### Dataset Summary
+This dataset contains URLs of news articles classified as either fake or satire. The articles classified as fake also have the URL of a rebutting article.
+
+### Supported Tasks and Leaderboards
+[More Information Needed]
+
+### Languages
+English
+
+## Dataset Structure
+
+### Data Instances
+```
+{
+"article_number": 102 ,
+"url_of_article": https://newslo.com/roger-stone-blames-obama-possibility-trump-alzheimers-attacks-president-caused-severe-stress/ ,
+"fake_or_satire": 1,  # Fake
+"url_of_rebutting_article": https://www.snopes.com/fact-check/donald-trumps-intelligence-quotient/
+}
+```
+
+### Data Fields
+- article_number: An integer used as an index for each row
+- url_of_article: A string which contains URL of an article to be assessed and classified as either Fake or Satire
+- fake_or_satire: A classlabel for the above variable which can take two values- Fake (1) and Satire (0)
+- url_of_rebutting_article: A string which contains a URL of the article used to refute the article in question (present - in url_of_article)
+
+### Data Splits
+This dataset is not split, only the train split is available.
+
+## 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
+Golbeck, Jennifer
+Everett, Jennine 
+Falak, Waleed
+Gieringer, Carl
+Graney, Jack 
+Hoffman, Kelly 
+Huth, Lindsay 
+Ma, Zhenya 
+Jha, Mayanka 
+Khan, Misbah 
+Kori, Varsha 
+Mauriello, Matthew 
+Lewis, Elo 
+Mirano, George 
+IV, William 
+Mussenden, Sean 
+Nelson, Tammie 
+Mcwillie, Sean 
+Pant, Akshat 
+Cheakalos, Paul
+
+### Licensing Information
+[More Information Needed]
+
+### Citation Information
+@inproceedings{inproceedings,
+author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},
+year = {2018},
+month = {05},
+pages = {17-21},
+title = {Fake News vs Satire: A Dataset and Analysis},
+doi = {10.1145/3201064.3201100}
+}
+
+### Contributions
+
+Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

huggingface_dataset/Dataset_Card/ghomasHudson_hotpotExtendedAno.md

@@ -0,0 +1,3 @@
+# hotpotQA-Extended (Annotated)
+
+A version of [HotpotQA-Extended](https://huggingface.co/datasets/ghomasHudson/hotpotExtended) with extra annotations about what part of the input contains the answer.

huggingface_dataset/Dataset_Card/graphs-datasets_AQSOL.md

@@ -0,0 +1,99 @@
+---
+license: mit
+task_categories:
+- graph-ml
+---
+
+# Dataset Card for AQSOL
+
+## 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)
+- [External Use](#external-use)
+  - [PyGeometric](#pygeometric)
+- [Dataset Structure](#dataset-structure)
+  - [Data Properties](#data-properties)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+- **[Homepage](https://github.com/graphdeeplearning/benchmarking-gnns)**
+- **Paper:**:  (see citation)
+
+
+### Dataset Summary
+The AQSOL dataset comes "from the Benchmarking Graph Neural Networks paper based on AqSolDB, a standardized database of 9,982 molecular graphs with their aqueous solubility values, collected from 9 different data sources" (PyGeometric doc).
+
+### Supported Tasks and Leaderboards
+`AQSOL` should be used for graph regression, on aqueous solubility. 
+
+## External Use
+### PyGeometric
+To load in PyGeometric, do the following:
+
+```python
+from datasets import load_dataset
+
+from torch_geometric.data import Data
+from torch_geometric.loader import DataLoader
+
+dataset_hf = load_dataset("graphs-datasets/<mydataset>")
+# For the train set (replace by valid or test as needed)
+dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
+dataset_pg = DataLoader(dataset_pg_list)
+```
+
+## Dataset Structure
+
+### Data Properties
+| property | value |
+|---|---|
+| #graphs | 9,833 |
+| average #nodes | 17.6 |
+| average #edges | 35.8 |
+
+### Data Fields
+
+Each row of a given file is a graph, with: 
+- `node_feat` (list: #nodes x #node-features): nodes
+- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
+- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
+- `y` (list:  #labels): contains the number of labels available to predict
+- `num_nodes` (int): number of nodes of the graph
+
+### Data Splits
+
+This data is split. It comes from the PyGeometric version of the dataset.
+
+## Additional Information
+
+### Licensing Information
+The dataset has been released under MIT license.
+
+### Citation Information
+```
+@article{DBLP:journals/corr/abs-2003-00982,
+  author    = {Vijay Prakash Dwivedi and
+               Chaitanya K. Joshi and
+               Thomas Laurent and
+               Yoshua Bengio and
+               Xavier Bresson},
+  title     = {Benchmarking Graph Neural Networks},
+  journal   = {CoRR},
+  volume    = {abs/2003.00982},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2003.00982},
+  eprinttype = {arXiv},
+  eprint    = {2003.00982},
+  timestamp = {Sat, 23 Jan 2021 01:14:30 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2003-00982.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+```

huggingface_dataset/Dataset_Card/huggan_vangogh2photo.md

@@ -0,0 +1,22 @@
+This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
+
+# Citation
+```
+@article{DBLP:journals/corr/ZhuPIE17,
+  author    = {Jun{-}Yan Zhu and
+               Taesung Park and
+               Phillip Isola and
+               Alexei A. Efros},
+  title     = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
+               Networks},
+  journal   = {CoRR},
+  volume    = {abs/1703.10593},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1703.10593},
+  eprinttype = {arXiv},
+  eprint    = {1703.10593},
+  timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

huggingface_dataset/Dataset_Card/huggingartists_loverance.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/loverance"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of the generated dataset:** 0.079792 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/a8a06b82765b2451bf65b21cf4384901.291x291x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/loverance">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">LoveRance</div>
+    <a href="https://genius.com/artists/loverance">
+    	<div style="text-align: center; font-size: 14px;">@loverance</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/loverance).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/loverance")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|23|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/loverance")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/huggingartists_sektor-gaza.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/sektor-gaza"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of the generated dataset:** 0.365256 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/9371696fb5660d2331d596bb29e5e91c.720x720x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/sektor-gaza">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">Сектор Газа (Sektor Gaza)</div>
+    <a href="https://genius.com/artists/sektor-gaza">
+    	<div style="text-align: center; font-size: 14px;">@sektor-gaza</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/sektor-gaza).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/sektor-gaza")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|155|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/sektor-gaza")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/huggingartists_sia.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/sia"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of the generated dataset:** 1.038296 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/aba931aaf48b7728f3f4869b13eb9741.1000x1000x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/sia">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">Sia</div>
+    <a href="https://genius.com/artists/sia">
+    	<div style="text-align: center; font-size: 14px;">@sia</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/sia).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/sia")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|742|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/sia")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/pariajm_sharif_emotional_speech_dataset.md

@@ -0,0 +1,75 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- fa
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+pretty_name: Sharif Emotional Speech Dataset (ShEMO)
+size_categories:
+- 1K<n<10K
+source_datasets:
+- radio-plays
+task_categories:
+- automatic-speech-recognition
+task_ids:
+- speech-recognition
+---
+
+## Sharif Emotional Speech Dataset (ShEMO)
+
+## Dataset Summary
+The dataset includes 3000 semi-natural utterances, equivalent to 3 hours and 25 minutes of speech data extracted from online Persian radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including <i>anger</i>, <i>fear</i>, <i>happiness</i>, <i>sadness</i> and <i>surprise</i>, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure, 
+the inter-annotator agreement is 64% which is interpreted as "substantial agreement".
+
+## Languages
+Persian (fa)
+
+## Overview of ShEMO
+Feature                      | Status   
+-------------                | ----------
+**license**                  | apache-2.0
+**language**                 | Persian (fa)
+**modality**                 | Speech
+**duration**                 | 3 hours and 25 minutes
+**#utterances**              | 3000
+**#speakers**                | 87 (31 females, 56 males)
+**#emotions**                | 5 basic emotions (anger, fear, happiness, sadness and surprise) and neutral state
+**orthographic transcripts** | Available
+**phonetic transcripts**     | Available
+
+## Data Instances
+Here is a sample of data instances:
+```json
+"F21N37": {
+    "speaker_id": "F21", 
+    "gender": "female", 
+    "emotion": "neutral", 
+    "transcript": "مگه من به تو نگفته بودم که باید راجع به دورانت سکوت کنی؟", 
+    "ipa": "mӕge mæn be to nægofte budӕm ke bɑyæd rɑdʒeʔ be dorɑnt sokut koni"
+ }
+```
+
+## Citation
+If you use this dataset, please cite the following paper:
+~~~~
+@Article{MohamadNezami2019,
+author  = {Mohamad Nezami, Omid and Jamshid Lou, Paria and Karami, Mansoureh},
+title = {ShEMO: a large-scale validated database for Persian speech emotion detection},
+journal = {Language Resources and Evaluation},
+year  = {2019},
+volume  = {53},
+number  = {1},
+pages = {1--16},
+issn  = {1574-0218},
+doi = {10.1007/s10579-018-9427-x},
+url = {https://doi.org/10.1007/s10579-018-9427-x}
+}
+~~~~
+
+## Download Dataset
+To download the dataset, please check the [ShEMO repo](https://github.com/pariajm/sharif-emotional-speech-database)!

huggingface_dataset/Dataset_Card/qgallouedec_prj_gia_dataset_metaworld_pick_out_of_hole_v2_1111.md

@@ -0,0 +1,36 @@
+---
+library_name: gia
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- gia
+- multi-task
+- multi-modal
+- imitation-learning
+- offline-reinforcement-learning
+---
+
+An imitation learning environment for the pick-out-of-hole-v2 environment, sample for the policy pick-out-of-hole-v2 
+
+This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia 
+
+
+
+
+## Load dataset
+
+First, clone it with
+
+```sh
+git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_pick_out_of_hole_v2_1111
+```
+
+Then, load it with
+
+```python
+import numpy as np
+dataset = np.load("prj_gia_dataset_metaworld_pick_out_of_hole_v2_1111/dataset.npy", allow_pickle=True).item()
+print(dataset.keys())  # dict_keys(['observations', 'actions', 'dones', 'rewards'])
+```
+
+    

huggingface_dataset/Dataset_Card/wikicorpus.md

@@ -0,0 +1,288 @@
+---
+pretty_name: Wikicorpus
+annotations_creators:
+- machine-generated
+- no-annotation
+language_creators:
+- found
+language:
+- ca
+- en
+- es
+license:
+- gfdl
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+- 10M<n<100M
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- fill-mask
+- text-classification
+- text-generation
+- token-classification
+task_ids:
+- language-modeling
+- masked-language-modeling
+- part-of-speech
+paperswithcode_id: null
+configs:
+- raw_ca
+- raw_en
+- raw_es
+- tagged_ca
+- tagged_en
+- tagged_es
+tags:
+- word-sense-disambiguation
+- lemmatization
+dataset_info:
+- config_name: raw_ca
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 263170192
+    num_examples: 143883
+  download_size: 96437841
+  dataset_size: 263170192
+- config_name: raw_es
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 671295359
+    num_examples: 259409
+  download_size: 252926918
+  dataset_size: 671295359
+- config_name: raw_en
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3388801074
+    num_examples: 1359146
+  download_size: 1346378932
+  dataset_size: 3388801074
+- config_name: tagged_ca
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: lemmas
+    sequence: string
+  - name: pos_tags
+    sequence: string
+  - name: wordnet_senses
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 1666129919
+    num_examples: 2016221
+  download_size: 226390380
+  dataset_size: 1666129919
+- config_name: tagged_es
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: lemmas
+    sequence: string
+  - name: pos_tags
+    sequence: string
+  - name: wordnet_senses
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 4100040390
+    num_examples: 5039367
+  download_size: 604910899
+  dataset_size: 4100040390
+- config_name: tagged_en
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: lemmas
+    sequence: string
+  - name: pos_tags
+    sequence: string
+  - name: wordnet_senses
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 18077275300
+    num_examples: 26350272
+  download_size: 2477450893
+  dataset_size: 18077275300
+---
+
+# Dataset Card for Wikicorpus
+
+## 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.cs.upc.edu/~nlp/wikicorpus/
+- **Repository:**
+- **Paper:** https://www.cs.upc.edu/~nlp/papers/reese10.pdf
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words.
+
+The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Each sub-dataset is monolingual in the languages:
+- ca: Catalan
+- en: English
+- es: Spanish
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The WikiCorpus is licensed under the same license as Wikipedia, that is, the [GNU Free Documentation License](http://www.fsf.org/licensing/licenses/fdl.html)
+
+### Citation Information
+
+```
+@inproceedings{reese-etal-2010-wikicorpus,
+    title = "{W}ikicorpus: A Word-Sense Disambiguated Multilingual {W}ikipedia Corpus",
+    author = "Reese, Samuel  and
+      Boleda, Gemma  and
+      Cuadros, Montse  and
+      Padr{\'o}, Llu{\'i}s  and
+      Rigau, German",
+    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
+    month = may,
+    year = "2010",
+    address = "Valletta, Malta",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/222_Paper.pdf",
+    abstract = "This article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, this is the largest such corpus that is freely available to the community: In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. We present a first attempt at creating a trilingual lexical resource from the sense-tagged Wikipedia corpora, namely, WikiNet. Moreover, we present two by-products of the project that are of use for the NLP community: An open source Java-based parser for Wikipedia pages developed for the construction of the corpus, and the integration of the WSD algorithm UKB in FreeLing.",
+}
+```
+
+### Contributions
+
+Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.