Upload batch 78 (20 files, last=huggingface_dataset/Dataset_Card/TomTBT_pmc_open_access_xml.md)

huggingface_dataset/Dataset_Card/LRGB_coco_superpixels_edge_wt_coord_feat_10.md

@@ -0,0 +1,38 @@
+---
+task_categories:
+- graph-ml
+size_categories:
+- 1M<n<10M
+tags:
+- lrgb
+license: cc-by-4.0
+---
+
+# `coco_superpixels_edge_wt_only_feat_10`
+
+### 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_land_style.md

@@ -0,0 +1,57 @@
+---
+language:
+- en
+tags:
+- stable-diffusion
+- text-to-image
+license: creativeml-openrail-m
+inference: false
+
+---
+
+# Landscape Style 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
+
+Two different Versions:
+
+### Version 1:
+
+File: ```land_style```
+
+To use it in a prompt: ```"art by land_style"```
+
+For best use write something like ```highly detailed background art by land_style```
+
+### Version 2:
+
+File: ```landscape_style```
+
+To use it in a prompt: ```"art by landscape_style"```
+
+For best use write something like ```highly detailed background art by landscape_style```
+
+If it is to strong just add [] around it.
+
+Trained until 7000 steps
+
+Have fun :)
+
+## Example Pictures
+
+<img src=https://i.imgur.com/UjoXFkJ.png width=100% height=100%/>
+<img src=https://i.imgur.com/rAoEyLK.png width=100% height=100%/>
+<img src=https://i.imgur.com/SpPsc7i.png width=100% height=100%/>
+<img src=https://i.imgur.com/zMH0EeI.png width=100% height=100%/>
+<img src=https://i.imgur.com/iQe0Jxc.png width=100% height=100%/>
+
+## 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/TomTBT_pmc_open_access_xml.md

@@ -0,0 +1,219 @@
+---
+pretty_name: XML-parsed PMC
+task_categories:
+- text-classification
+- summarization
+- other
+annotations_creators:
+- no-annotation
+language_creators:
+- expert-generated
+language:
+- en
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+license:
+- cc0-1.0
+- cc-by-4.0
+- cc-by-sa-4.0
+- cc-by-nc-4.0
+- cc-by-nd-4.0
+- cc-by-nc-nd-4.0
+- cc-by-nc-sa-4.0
+- unknown
+- other
+multilinguality:
+- monolingual
+task_ids: []
+tags:
+- research papers
+- biology
+- medecine
+---
+
+# Dataset Card for PMC Open Access XML
+
+## 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://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+The XML Open Access includes more than 3.4 million journal articles and preprints that are made available under
+license terms that allow reuse. 
+Not all articles in PMC are available for text mining and other reuse, many have copyright protection, however articles
+in the PMC Open Access Subset are made available under Creative Commons or similar licenses that generally allow more
+liberal redistribution and reuse than a traditional copyrighted work. 
+The PMC Open Access Subset is one part of the PMC Article Datasets
+
+This version takes XML version as source, benefiting from the structured text
+to split the articles in parts, naming the introduction, methods, results,
+discussion and conclusion, and reference with keywords in the text to external or internal
+resources (articles, figures, tables, formulas, boxed-text, quotes, code, footnotes, chemicals, graphics, medias).
+
+The dataset was initially created with relation-extraction tasks in mind, between the references in text and the content of the
+references (e.g. for PMID, by joining the refered article abstract from the pubmed dataset), but aims in a larger extent to provide
+a corpus of pre-annotated text for other tasks (e.g. figure caption to graphic, glossary definition detection, summarization).
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+[Needs More Information]
+
+## Dataset Structure
+
+### Data Fields
+
+- "accession_id": The PMC ID of the article
+- "pmid":         The PubMed ID of the article
+- "introduction": List of \<title\> and \<p\> elements in \<body\>, sharing their root with a \<title\> containing "introduction" or "background".
+- "methods":      Same as introduction with "method" keyword.
+- "results":      Same as introduction with "result" keyword.
+- "discussion":   Same as introduction with "discussion" keyword.
+- "conclusion":   Same as introduction with "conclusion" keyword. 
+- "front":        List of \<title\> and \<p\> elements in \<front\> after everything else has been searched.
+- "body":         List of \<title\> and \<p\> elements in \<body\> after everything else has been searched.
+- "back":         List of \<title\> and \<p\> elements in \<back\> after everything else has been searched.
+- "figure":       List of \<fig\> elements of the article.
+- "table":        List of \<table-wrap\> and \<array\> elements of the article.
+- "formula":      List of \<disp-formula\> and \<inline-formula\> elements of the article.
+- "box":          List of \<boxed-text\> elements of the article.
+- "code":         List of \<code\> elements of the article.
+- "quote":        List of \<disp-quote\> and \<speech\> elements of the article.
+- "chemical":     List of \<chem-struct-wrap\> elements of the article.
+- "supplementary": List of \<supplementary-material\> and \<inline-supplementary-material\> elements of the article.
+- "footnote":      List of \<fn-group\> and \<table-wrap-foot\> elements of the article.
+- "graphic":       List of \<graphic\> and \<inline-graphic\> elements of the article.
+- "media":         List of \<media\> and \<inline-media\> elements of the article.
+- "glossary": Glossary if found in the XML
+- "unknown_references": JSON of a dictionnary of each "tag":"text" for the reference that did not indicate a PMID
+- "n_references": Total number of references and unknown references
+- "license": The licence of the article
+- "retracted": If the article was retracted or not
+- "last_updated": Last update of the article
+- "citation": Citation of the article
+- "package_file": path to the folder containing the graphics and media files of the article (to append to the base URL: ftp.ncbi.nlm.nih.gov/pub/pmc/)
+
+In text, the references are in the form ##KEYWORD##IDX_REF##OLD_TEXT##, with keywords (REF, UREF, FIG, TAB, FORMU, BOX, CODE, QUOTE, CHEM, SUPPL, FOOTN, GRAPH, MEDIA) referencing respectively to "pubmed articles" (external), "unknown_references", "figure", "table", "formula", "box", "code", "quote", "chem", "supplementary", "footnote", "graphic" and "media".
+### Data Splits
+
+[Needs More Information]
+
+## Dataset Creation
+
+### Curation Rationale
+
+Internal references (figures, tables, ...) were found using specific tags. Deciding on those tags was done by testing and by looking in the documentation
+for the different kind of possible usage.
+Then, to split the article into introduction, methods, results, discussion and conclusion, specific keywords in titles were used. Because there are no rules
+in this xml to tag those sections, finding the keyword seemed like the most reliable approach to do so. A drawback is that many section do not have those 
+keywords in the titles but could be assimilated to those. However, the huge diversity in the titles makes it harder to label such sections. This could be the
+work of further versions of this dataset.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was obtained from:
+- ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/oa_noncomm/xml/
+- ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/oa_comm/xml/
+- ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/oa_other/xml/
+
+Additional content for individual articles (graphics, media) can be obtained from:
+- ftp.ncbi.nlm.nih.gov/pub/pmc + "package_file"
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### 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
+
+The articles XML are similar accross collections. This means that if a certain collection handles the structure in unusual ways, the whole collection might not be as
+well annotated than others. This concerns all the sections (intro, methods, ...), the external references (pmids) and the internal references (tables, figures, ...).
+To illustrate that, references are sometime given as a range (e.g. 10-15). In that case, only reference 10 and 15 are linked. This could potentially be handled in a
+future version.
+
+### Other Known Limitations
+
+[Needs More Information]
+
+### Preprocessing recommendations
+
+- Filter out empty contents.
+- Remove unwanted references from the text, and replace either by the "references_text" or by the reference content itself.
+- Unescape HTML special characters: `import html; html.unescape(my_text)`
+- Remove superfluous line break in text.
+- Remove XML tags (\<italic\>, \<sup\>, \<sub\>, ...), replace by special tokens? 
+- Join the items of the contents' lists.
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+https://www.ncbi.nlm.nih.gov/pmc/about/copyright/
+
+Within the PMC Open Access Subset, there are three groupings:
+
+Commercial Use Allowed - CC0, CC BY, CC BY-SA, CC BY-ND licenses
+Non-Commercial Use Only - CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses; and
+Other - no machine-readable Creative Commons license, no license, or a custom license.
+
+### Citation Information
+
+[Needs More Information]

huggingface_dataset/Dataset_Card/abhishek_autonlp-data-prodigy-10.md

@@ -0,0 +1,1920 @@
+---
+language:
+- en
+---
+# AutoNLP Dataset for project: prodigy-10
+
+## Table of content
+- [Dataset Description](#dataset-description)
+    - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+
+## Dataset Descritpion
+
+This dataset has been automatically processed by AutoNLP for project prodigy-10.
+
+### Languages
+
+The BCP-47 code for the dataset's language is en.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from this dataset looks as follows:
+
+```json
+[
+  {
+    "tags": [
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8
+    ],
+    "tokens": [
+      "tory",
+      "backing",
+      "for",
+      "i",
+      "d",
+      "cards",
+      "the",
+      "tories",
+      "are",
+      "to",
+      "back",
+      "controversial",
+      "government",
+      "plans",
+      "to",
+      "introduce",
+      "i",
+      "d",
+      "cards",
+      ".",
+      " ",
+      "the",
+      "shadow",
+      "cabinet",
+      "revealed",
+      "its",
+      "support",
+      "ahead",
+      "of",
+      "next",
+      "week",
+      "s",
+      "commons",
+      "vote",
+      "on",
+      "a",
+      "bill",
+      "to",
+      "introduce",
+      "compulsory",
+      "i",
+      "d.",
+      "the",
+      "decision",
+      "follows",
+      "a",
+      " ",
+      "tough",
+      "meeting",
+      " ",
+      "where",
+      "some",
+      "senior",
+      "tories",
+      "argued",
+      "vociferously",
+      "against",
+      "the",
+      "move",
+      " ",
+      "party",
+      "sources",
+      "told",
+      "the",
+      "bbc",
+      ".",
+      "the",
+      "bill",
+      " ",
+      "which",
+      "ministers",
+      "claim",
+      "will",
+      "tackle",
+      "crime",
+      " ",
+      "terrorism",
+      "and",
+      "illegal",
+      "immigration",
+      " ",
+      "is",
+      "expected",
+      "to",
+      "be",
+      "opposed",
+      "by",
+      "the",
+      "liberal",
+      "democrats",
+      ".",
+      " ",
+      "they",
+      "have",
+      "said",
+      "the",
+      "scheme",
+      "is",
+      " ",
+      "deeply",
+      "flawed",
+      " ",
+      "and",
+      "a",
+      "waste",
+      "of",
+      "money",
+      ".",
+      "sources",
+      "within",
+      "the",
+      "conservative",
+      "party",
+      "told",
+      "the",
+      "bbc",
+      "michael",
+      "howard",
+      "has",
+      "always",
+      "been",
+      "in",
+      "favour",
+      "of",
+      "i",
+      "d",
+      "cards",
+      " ",
+      "and",
+      "tried",
+      "to",
+      "introduce",
+      "them",
+      "when",
+      "he",
+      "was",
+      "home",
+      "secretary",
+      ".",
+      "the",
+      "party",
+      "has",
+      "been",
+      " ",
+      "agnostic",
+      " ",
+      "on",
+      "the",
+      "issue",
+      "until",
+      "now",
+      "but",
+      "had",
+      "now",
+      "decided",
+      "to",
+      "come",
+      "off",
+      "the",
+      "fence",
+      " ",
+      "the",
+      "tory",
+      "source",
+      "said",
+      ".",
+      "despite",
+      "giving",
+      "their",
+      "backing",
+      "to",
+      "i",
+      "d",
+      "cards",
+      " ",
+      "the",
+      "conservatives",
+      "insisted",
+      "they",
+      "would",
+      "hold",
+      "ministers",
+      "to",
+      "account",
+      "over",
+      "the",
+      "precise",
+      "purpose",
+      "of",
+      "the",
+      "scheme",
+      ".",
+      " ",
+      "they",
+      "said",
+      "they",
+      "would",
+      "also",
+      "press",
+      "labour",
+      "over",
+      "whether",
+      "objectives",
+      "could",
+      "be",
+      "met",
+      "and",
+      "whether",
+      "the",
+      "home",
+      "office",
+      "would",
+      "deliver",
+      "them",
+      ".",
+      "and",
+      "they",
+      "pledged",
+      "to",
+      "assess",
+      "the",
+      "cost",
+      "effectiveness",
+      "of",
+      "i",
+      "d",
+      "cards",
+      "and",
+      "whether",
+      "people",
+      "s",
+      "privacy",
+      "would",
+      "be",
+      "properly",
+      "protected",
+      ".",
+      " ",
+      "it",
+      "is",
+      "important",
+      "to",
+      "remember",
+      "that",
+      "this",
+      "bill",
+      "will",
+      "take",
+      "a",
+      "decade",
+      "to",
+      "come",
+      "into",
+      "full",
+      "effect",
+      "  ",
+      "a",
+      "spokesman",
+      "said",
+      ".",
+      " ",
+      "it",
+      "will",
+      "do",
+      "nothing",
+      "to",
+      "solve",
+      "the",
+      "immediate",
+      "problems",
+      "of",
+      "rising",
+      "crime",
+      "and",
+      "uncontrolled",
+      "immigration",
+      ".",
+      "  ",
+      "lib",
+      "dem",
+      "home",
+      "affairs",
+      "spokesman",
+      "mark",
+      "oaten",
+      "said",
+      ":",
+      " ",
+      "this",
+      "has",
+      "all",
+      "the",
+      "signs",
+      "of",
+      "michael",
+      "howard",
+      "overruling",
+      "colleagues",
+      " ",
+      "concerns",
+      "over",
+      "i",
+      "d",
+      "cards",
+      ".",
+      " ",
+      "the",
+      "tories",
+      "should",
+      "have",
+      "the",
+      "courage",
+      "to",
+      "try",
+      "and",
+      "change",
+      "public",
+      "opinion",
+      "not",
+      "follow",
+      "it",
+      ".",
+      " ",
+      "the",
+      "new",
+      "chairman",
+      "of",
+      "the",
+      "bar",
+      "council",
+      " ",
+      "guy",
+      "mansfield",
+      "qc",
+      "warned",
+      "there",
+      "was",
+      "a",
+      "real",
+      "risk",
+      "that",
+      "people",
+      "on",
+      "the",
+      " ",
+      "margins",
+      "of",
+      "society",
+      " ",
+      "would",
+      "be",
+      "driven",
+      "into",
+      "the",
+      "hands",
+      "of",
+      "extremists",
+      ".",
+      " ",
+      "what",
+      "is",
+      "going",
+      "to",
+      "happen",
+      "to",
+      "young",
+      "asian",
+      "men",
+      "when",
+      "there",
+      "has",
+      "been",
+      "a",
+      "bomb",
+      "gone",
+      "off",
+      "somewhere",
+      " ",
+      "they",
+      "are",
+      "going",
+      "to",
+      "be",
+      "stopped",
+      ".",
+      "if",
+      "they",
+      "haven",
+      "t",
+      "[",
+      "i",
+      "d",
+      "cards",
+      "]",
+      "they",
+      "are",
+      "going",
+      "to",
+      "be",
+      "detained",
+      "."
+    ]
+  },
+  {
+    "tags": [
+      2,
+      6,
+      8,
+      8,
+      0,
+      8,
+      0,
+      8,
+      8,
+      8,
+      2,
+      6,
+      6,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      0,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      1,
+      5,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      0,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      0,
+      8,
+      8,
+      8,
+      0,
+      8,
+      2,
+      6,
+      8,
+      2,
+      6,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      0,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      2,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      0,
+      8,
+      2,
+      6,
+      6,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      0,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      0,
+      8,
+      2,
+      6,
+      6,
+      8,
+      8,
+      8,
+      0,
+      8,
+      2,
+      6,
+      8,
+      8,
+      8,
+      8,
+      2,
+      6,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      2,
+      8,
+      8,
+      8,
+      8,
+      8,
+      2,
+      6,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      0,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      2,
+      6,
+      8,
+      8,
+      2,
+      8,
+      8,
+      0,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      2,
+      6,
+      8,
+      8,
+      0,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      2,
+      6,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      8,
+      2,
+      6,
+      8
+    ],
+    "tokens": [
+      "o",
+      "gara",
+      "revels",
+      "in",
+      "ireland",
+      "victory",
+      "ireland",
+      "fly",
+      "-",
+      "half",
+      "ronan",
+      "o",
+      "gara",
+      "hailed",
+      "his",
+      "side",
+      "s",
+      "19",
+      "-",
+      "13",
+      "victory",
+      "over",
+      "england",
+      "as",
+      "a",
+      " ",
+      "special",
+      " ",
+      "win",
+      ".",
+      " ",
+      "the",
+      "munster",
+      "number",
+      "10",
+      "kicked",
+      "a",
+      "total",
+      "of",
+      "14",
+      "points",
+      " ",
+      "including",
+      "two",
+      "drop",
+      "goals",
+      " ",
+      "to",
+      "help",
+      "keep",
+      "alive",
+      "their",
+      "grand",
+      "slam",
+      "hopes",
+      ".",
+      "he",
+      "told",
+      "bbc",
+      "sport",
+      ":",
+      " ",
+      "we",
+      "made",
+      "hard",
+      "work",
+      "of",
+      "it",
+      "but",
+      "it",
+      "s",
+      "still",
+      "special",
+      "to",
+      "beat",
+      "england",
+      ".",
+      " ",
+      "i",
+      "had",
+      "three",
+      "chances",
+      "to",
+      "win",
+      "the",
+      "game",
+      "but",
+      "didn",
+      "t.",
+      "we",
+      "have",
+      "work",
+      "to",
+      "do",
+      "after",
+      "this",
+      "but",
+      "we",
+      "never",
+      "take",
+      "a",
+      "victory",
+      "over",
+      "england",
+      "lightly",
+      ".",
+      " ",
+      "ireland",
+      "hooker",
+      "shane",
+      "byrne",
+      "echoed",
+      "o",
+      "gara",
+      "s",
+      "comments",
+      "but",
+      "admitted",
+      "the",
+      "game",
+      "had",
+      "been",
+      "england",
+      "s",
+      "best",
+      "outing",
+      "in",
+      "the",
+      "six",
+      "nations",
+      ".",
+      "byrne",
+      "said",
+      ":",
+      " ",
+      "it",
+      "was",
+      "a",
+      "really",
+      " ",
+      "really",
+      "hard",
+      "game",
+      "but",
+      "from",
+      "one",
+      "to",
+      "15",
+      "in",
+      "our",
+      "team",
+      "we",
+      "worked",
+      "really",
+      " ",
+      "really",
+      "hard",
+      ".",
+      " ",
+      "we",
+      "just",
+      "had",
+      "to",
+      "stick",
+      "to",
+      "our",
+      "defensive",
+      "pattern",
+      " ",
+      "trust",
+      "ourselves",
+      "and",
+      "trust",
+      "those",
+      "around",
+      "us",
+      ".",
+      "all",
+      "round",
+      "it",
+      "was",
+      "fantastic",
+      ".",
+      " ",
+      "ireland",
+      "captain",
+      "brian",
+      "o",
+      "driscoll",
+      " ",
+      "who",
+      "scored",
+      "his",
+      "side",
+      "s",
+      "only",
+      "try",
+      " ",
+      "said",
+      ":",
+      " ",
+      "we",
+      "are",
+      "delighted",
+      " ",
+      "we",
+      "felt",
+      "if",
+      "we",
+      "performed",
+      "well",
+      "then",
+      "we",
+      "would",
+      "win",
+      "but",
+      "with",
+      "england",
+      "also",
+      "having",
+      "played",
+      "very",
+      "well",
+      "it",
+      "makes",
+      "it",
+      "all",
+      "the",
+      "sweeter",
+      ".",
+      " ",
+      "we",
+      "did",
+      "get",
+      "the",
+      "bounce",
+      "of",
+      "the",
+      "ball",
+      "and",
+      "some",
+      "days",
+      "that",
+      "happens",
+      "and",
+      "you",
+      "ve",
+      "just",
+      "got",
+      "to",
+      "jump",
+      "on",
+      "the",
+      "back",
+      "of",
+      "it",
+      ".",
+      "  ",
+      "ireland",
+      "coach",
+      "eddie",
+      "o",
+      "sullivan",
+      "was",
+      "surprised",
+      "that",
+      "england",
+      "coach",
+      "andy",
+      "robinson",
+      "said",
+      "he",
+      "was",
+      "certain",
+      "mark",
+      "cueto",
+      "was",
+      "onside",
+      "for",
+      "a",
+      "disallowed",
+      "try",
+      "just",
+      "before",
+      "the",
+      "break",
+      ".",
+      " ",
+      "andy",
+      "was",
+      "sitting",
+      "two",
+      "yards",
+      "from",
+      "me",
+      "and",
+      "i",
+      "couldn",
+      "t",
+      "see",
+      "whether",
+      "he",
+      "was",
+      "offside",
+      "or",
+      "not",
+      "so",
+      "i",
+      "don",
+      "t",
+      "know",
+      "how",
+      "andy",
+      "could",
+      "have",
+      "known",
+      "  ",
+      "said",
+      "o",
+      "sullivan",
+      ".",
+      "  ",
+      "what",
+      "i",
+      "do",
+      "know",
+      "is",
+      "that",
+      "england",
+      "played",
+      "well",
+      "and",
+      "when",
+      "that",
+      "happens",
+      "it",
+      "makes",
+      "a",
+      "very",
+      "good",
+      "victory",
+      "for",
+      "us",
+      ".",
+      " ",
+      "we",
+      "had",
+      "to",
+      "defend",
+      "for",
+      "long",
+      "periods",
+      "and",
+      "that",
+      "is",
+      "all",
+      "good",
+      "for",
+      "the",
+      "confidence",
+      "of",
+      "the",
+      "team",
+      ".",
+      " ",
+      "i",
+      "think",
+      "our",
+      "try",
+      "was",
+      "very",
+      "well",
+      "worked",
+      " ",
+      "it",
+      "was",
+      "a",
+      "gem",
+      " ",
+      "as",
+      "good",
+      "a",
+      "try",
+      "as",
+      "we",
+      "have",
+      "scored",
+      "for",
+      "a",
+      "while",
+      ".",
+      " ",
+      "o",
+      "sullivan",
+      "also",
+      "rejected",
+      "robinson",
+      "s",
+      "contention",
+      "england",
+      "dominated",
+      "the",
+      "forward",
+      "play",
+      ".",
+      " ",
+      "i",
+      "think",
+      "we",
+      "lost",
+      "one",
+      "lineout",
+      "and",
+      "they",
+      "lost",
+      "four",
+      "or",
+      "five",
+      "so",
+      "i",
+      "don",
+      "t",
+      "know",
+      "how",
+      "that",
+      "adds",
+      "up",
+      "to",
+      "domination",
+      "  ",
+      "he",
+      "said",
+      ".",
+      "o",
+      "driscoll",
+      "also",
+      "insisted",
+      "ireland",
+      "were",
+      "happy",
+      "to",
+      "handle",
+      "the",
+      "pressure",
+      "of",
+      "being",
+      "considered",
+      "favourites",
+      "to",
+      "win",
+      "the",
+      "six",
+      "nations",
+      "title",
+      ".",
+      " ",
+      "this",
+      "season",
+      "for",
+      "the",
+      "first",
+      "time",
+      "we",
+      "have",
+      "been",
+      "able",
+      "to",
+      "play",
+      "with",
+      "the",
+      "favourites",
+      " ",
+      "tag",
+      "  ",
+      "he",
+      "said",
+      ".",
+      " ",
+      "hopefully",
+      "we",
+      "have",
+      "proved",
+      "that",
+      "today",
+      "and",
+      "can",
+      "continue",
+      "to",
+      "keep",
+      "doing",
+      "so",
+      ".",
+      " ",
+      "as",
+      "for",
+      "my",
+      "try",
+      "it",
+      "was",
+      "a",
+      "move",
+      "we",
+      "had",
+      "worked",
+      "on",
+      "all",
+      "week",
+      ".",
+      "there",
+      "was",
+      "a",
+      "bit",
+      "of",
+      "magic",
+      "from",
+      "geordan",
+      "murphy",
+      "and",
+      "it",
+      "was",
+      "a",
+      "great",
+      "break",
+      "from",
+      "denis",
+      "hickie",
+      "."
+    ]
+  }
+]
+```
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "tags": "Sequence(feature=ClassLabel(num_classes=9, names=['B-LOCATION', 'B-ORG', 'B-PERSON', 'B-PRODUCT', 'I-LOCATION', 'I-ORG', 'I-PERSON', 'I-PRODUCT', 'O'], names_file=None, id=None), length=-1, id=None)",
+  "tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)"
+}
+```
+
+### Dataset Splits
+
+This dataset is split into a train and validation split. The split sizes are as follow:
+
+| Split name   | Num samples         |
+| ------------ | ------------------- |
+| train        | 186 |
+| valid        | 58 |

huggingface_dataset/Dataset_Card/anhdungitvn_sccr.md

@@ -0,0 +1,31 @@
+---
+license: apache-2.0
+---
+
+
+```python
+from datasets import load_dataset
+data_name = "anhdungitvn/sccr"
+data_files = {"train": "train.tsv", "eval": "eval.tsv"}
+sccr = load_dataset(data_name, data_files=data_files)
+sccr
+```
+
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['text', 'labels'],
+        num_rows: 14478
+    })
+    eval: Dataset({
+        features: ['text', 'labels'],
+        num_rows: 1609
+    })
+})
+```
+
+
+
+### References
+  - <a href="https://www.aivivn.com/contests/6">SC: Sentiment Classification (Phân loại sắc thái bình luận)</a>

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-sen_vi-894567-2175669982.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/feed
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-2.7b
+  metrics: []
+  dataset_name: futin/feed
+  dataset_config: sen_vi
+  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-2.7b
+* Dataset: futin/feed
+* Config: sen_vi
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en-6ca7d2-2087467163.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/guess
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloomz-1b1
+  metrics: []
+  dataset_name: futin/guess
+  dataset_config: en
+  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/bloomz-1b1
+* Dataset: futin/guess
+* Config: en
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-squad_v2-1e2c143e-1305549899.md

@@ -0,0 +1,35 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: nbroad/rob-base-superqa1
+  metrics: []
+  dataset_name: squad_v2
+  dataset_config: squad_v2
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: nbroad/rob-base-superqa1
+* 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 [@nbroad](https://huggingface.co/nbroad) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-61110342-7234758.md

@@ -0,0 +1,30 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- xtreme
+eval_info:
+  task: entity_extraction
+  model: transformersbook/xlm-roberta-base-finetuned-panx-de
+  dataset_name: xtreme
+  dataset_config: PAN-X.de
+  dataset_split: validation
+  col_mapping:
+    tokens: tokens
+    tags: ner_tags
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: transformersbook/xlm-roberta-base-finetuned-panx-de
+* Dataset: xtreme
+
+To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/clarin-pl_polemo2-official.md

@@ -0,0 +1,146 @@
+---
+annotations_creators: 
+- expert-generated 
+language_creators: 
+- other
+language: 
+- pl
+license: 
+- cc-by-sa-4.0
+multilinguality: 
+- monolingual
+pretty_name: 'Polemo2'
+size_categories: 
+- 8K
+- 1K<n<10K
+source_datasets: 
+- original
+task_categories: 
+- text-classification
+task_ids: 
+- sentiment-classification
+---
+
+# Polemo2
+
+## Description
+
+The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is annotated with four labels: positive, negative, neutral, or ambiguous.
+
+## Tasks (input, output and metrics)
+
+The task is to predict the correct label of the review.
+
+**Input** ('*text*' column): sentence
+
+**Output** ('*target*' column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous)
+
+**Domain**: Online reviews
+
+**Measurements**: Accuracy, F1 Macro
+
+**Example**: 
+
+Input: `Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach , brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych ilościach i nie smaczne . Nie polecam nikomu tego hotelu .`
+
+Input (translated by DeepL): `At the very entrance the hotel stinks . In the rooms there is mold on the walls , dirty carpet . The bathroom smells of chemicals , the hotel does not heat in the rooms are cold . The room furnishings are old , the faucet moves , the door to the balcony does not close . The food is in small quantities and not tasty . I would not recommend this hotel to anyone .`
+
+Output:  `1` (negative)
+
+## Data splits
+
+| Subset | Cardinality |
+|--------|------------:|
+| train  |        6573 |
+| val    |         823 |
+| test   |         820 |
+
+## Class distribution
+
+| Class   |   train |          dev |   test |
+|:--------|--------:|-------------:|-------:|
+| minus   |  0.3756 |       0.3694 | 0.4134 |
+| plus    |  0.2775 |       0.2868 | 0.2768 |
+| amb     |  0.1991 |       0.1883 | 0.1659 |
+| zero    |  0.1477 |       0.1555 | 0.1439 |
+
+## Citation
+
+```
+@inproceedings{kocon-etal-2019-multi,
+    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
+    author = "Koco{\'n}, Jan  and
+      Mi{\l}kowski, Piotr  and
+      Za{\'s}ko-Zieli{\'n}ska, Monika",
+    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/K19-1092",
+    doi = "10.18653/v1/K19-1092",
+    pages = "980--991",
+    abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
+}
+```
+
+## License
+
+```
+Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
+```
+
+## Links
+
+[HuggingFace](https://huggingface.co/datasets/clarin-pl/polemo2-official)
+
+[Source](https://clarin-pl.eu/dspace/handle/11321/710)
+
+[Paper](https://aclanthology.org/K19-1092/)
+
+## Examples
+
+### Loading
+
+```python
+from pprint import pprint
+
+from datasets import load_dataset
+
+dataset = load_dataset("clarin-pl/polemo2-official")
+pprint(dataset['train'][0])
+
+# {'target': 1,
+#  'text': 'Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach '
+#          ', brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w '
+#          'pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się '
+#          'rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych '
+#          'ilościach i nie smaczne . Nie polecam nikomu tego hotelu .'}
+```
+
+### Evaluation
+
+```python
+import random
+from pprint import pprint
+
+from datasets import load_dataset, load_metric
+
+dataset = load_dataset("clarin-pl/polemo2-official")
+references = dataset["test"]["target"]
+
+# generate random predictions
+predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
+
+acc = load_metric("accuracy")
+f1 = load_metric("f1")
+
+acc_score = acc.compute(predictions=predictions, references=references)
+f1_score = f1.compute(predictions=predictions, references=references, average='macro')
+
+pprint(acc_score)
+pprint(f1_score)
+
+# {'accuracy': 0.2475609756097561}
+# {'f1': 0.23747048177471738}
+```

huggingface_dataset/Dataset_Card/classla_FRENK-hate-hr.md

@@ -0,0 +1,117 @@
+---
+language:
+- hr
+license:
+- other
+size_categories:
+- 1K<n<10K
+task_categories:
+- text-classification
+task_ids: []
+tags:
+- hate-speech-detection
+- offensive-language
+---
+
+# Offensive language dataset of Croatian comments FRENK 1.0
+
+Croatian subset of the [FRENK dataset](http://hdl.handle.net/11356/1433). Also available on HuggingFace dataset hub: [English subset](https://huggingface.co/datasets/5roop/FRENK-hate-en),  [Slovenian subset](https://huggingface.co/datasets/5roop/FRENK-hate-sl).
+
+## Dataset Description
+
+- **Homepage:** http://hdl.handle.net/11356/1433
+- **Repository:** http://hdl.handle.net/11356/1433
+- **Paper:** https://arxiv.org/abs/1906.02045
+- **Project page** https://nl.ijs.si/frenk/
+
+## Description of the original dataset
+
+>The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [https://arxiv.org/pdf/1906.02045.pdf]. Usernames in the metadata are pseudo-anonymised and removed from the comments.
+>
+>The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.
+
+For this dataset only the Croatian data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split). Test segment has been preserved in its original form.
+
+## Usage in `Transformers`
+
+```python
+import datasets
+ds = datasets.load_dataset("classla/FRENK-hate-hr","binary") 
+```
+
+For binary classification the following encoding is used:
+
+
+```python
+_CLASS_MAP_BINARY = {
+    'Acceptable': 0, 
+    'Offensive': 1, 
+}
+```
+The original labels are available if the dataset is loaded with the `multiclass` option:
+
+```python
+import datasets
+ds = datasets.load_dataset("classla/FRENK-hate-hr","multiclass").
+```
+
+In this case the encoding used is:
+```python
+_CLASS_MAP_MULTICLASS = {
+    'Acceptable speech': 0, 
+    'Inappropriate': 1, 
+    'Background offensive': 2,
+    'Other offensive': 3, 
+    'Background violence': 4,
+    'Other violence': 5, 
+}
+```
+
+
+## Data structure
+
+* `text`: text
+* `target`: who is the target of the hate-speech text ("no target", "commenter", "target" (migrants or LGBT, depending on the topic), or "related to" (again, the topic))
+* `topic`: whether the text relates to lgbt or migrants hate-speech domains
+* `label`: label of the text instance, see above.
+
+## Data instance
+
+```
+{'text': 'Potpisujem komentar g ankice pavicic',
+ 'target': 'No target',
+ 'topic': 'lgbt',
+ 'label': 0}
+ ```
+
+## Licensing information
+
+CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0
+
+## Citation information
+
+When using this dataset please cite the following paper:
+
+```
+@misc{ljubešić2019frenk,
+      title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, 
+      author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
+      year={2019},
+      eprint={1906.02045},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/1906.02045}
+}
+```
+
+The original dataset can be cited as
+
+ ```
+@misc{11356/1433,
+ title = {Offensive language dataset of Croatian, English and Slovenian comments {FRENK} 1.0},
+ author = {Ljube{\v s}i{\'c}, Nikola and Fi{\v s}er, Darja and Erjavec, Toma{\v z}},
+ url = {http://hdl.handle.net/11356/1433},
+ note = {Slovenian language resource repository {CLARIN}.{SI}},
+ copyright = {{CLARIN}.{SI} Licence {ACA} {ID}-{BY}-{NC}-{INF}-{NORED} 1.0},
+ year = {2021} }
+ ```

huggingface_dataset/Dataset_Card/codeparrot_github-code.md

@@ -0,0 +1,239 @@
+---
+annotations_creators: []
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- code
+license:
+- other
+multilinguality:
+- multilingual
+pretty_name: github-code
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+---
+
+# GitHub Code Dataset
+
+## Dataset Description
+The GitHub Code dataset consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in 1TB of data. The dataset was created from the public GitHub dataset on Google BiqQuery.
+
+### How to use it
+
+The GitHub Code dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following two lines of code:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("codeparrot/github-code", streaming=True, split="train")
+print(next(iter(ds)))
+
+#OUTPUT:
+{
+ 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n",
+ 'repo_name': 'MirekSz/webpack-es6-ts',
+ 'path': 'app/mods/mod190.js',
+ 'language': 'JavaScript',
+ 'license': 'isc',
+ 'size': 73
+}
+```
+
+You can see that besides the code, repo name, and path also the programming language, license, and the size of the file are part of the dataset. You can also filter the dataset for any subset of the 30 included languages (see the full list below) in the dataset. Just pass the list of languages as a list. E.g. if your dream is to build a Codex model for Dockerfiles use the following configuration:
+
+```python
+ds = load_dataset("codeparrot/github-code", streaming=True, split="train", languages=["Dockerfile"])
+print(next(iter(ds))["code"])
+
+#OUTPUT:
+"""\
+FROM rockyluke/ubuntu:precise
+
+ENV DEBIAN_FRONTEND="noninteractive" \
+    TZ="Europe/Amsterdam"
+...
+"""
+```
+
+We also have access to the license of the origin repo of a file so we can filter for licenses in the same way we filtered for languages:
+ 
+```python
+ds = load_dataset("codeparrot/github-code", streaming=True, split="train", licenses=["mit", "isc"])
+
+licenses = []
+for element in iter(ds).take(10_000):
+    licenses.append(element["license"])
+print(Counter(licenses))
+
+#OUTPUT:
+Counter({'mit': 9896, 'isc': 104})
+```
+
+Naturally, you can also download the full dataset. Note that this will download ~300GB compressed text data and the uncompressed dataset will take up ~1TB of storage:
+```python
+ds = load_dataset("codeparrot/github-code", split="train")
+```
+
+## Data Structure
+
+### Data Instances
+
+```python
+{
+ 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n",
+ 'repo_name': 'MirekSz/webpack-es6-ts',
+ 'path': 'app/mods/mod190.js',
+ 'language': 'JavaScript',
+ 'license': 'isc',
+ 'size': 73
+}
+```
+
+### Data Fields
+
+|Field|Type|Description|
+|---|---|---|
+|code|string|content of source file|
+|repo_name|string|name of the GitHub repository|
+|path|string|path of file in GitHub repository|
+|language|string|programming language as inferred by extension|
+|license|string|license of GitHub repository|
+|size|int|size of source file in bytes|
+
+### Data Splits
+
+The dataset only contains a train split.
+
+## Languages
+
+The dataset contains 30 programming languages with over 60 extensions:
+
+```python
+{
+    "Assembly": [".asm"],
+    "Batchfile": [".bat", ".cmd"],
+    "C": [".c", ".h"],
+    "C#": [".cs"],
+    "C++": [".cpp", ".hpp", ".c++", ".h++", ".cc", ".hh", ".C", ".H"],
+    "CMake": [".cmake"],
+    "CSS": [".css"],
+    "Dockerfile": [".dockerfile", "Dockerfile"],
+    "FORTRAN": ['.f90', '.f', '.f03', '.f08', '.f77', '.f95', '.for', '.fpp'],
+    "GO": [".go"],
+    "Haskell": [".hs"],
+    "HTML":[".html"],
+    "Java": [".java"],
+    "JavaScript": [".js"],
+    "Julia": [".jl"],
+    "Lua": [".lua"],
+    "Makefile": ["Makefile"],
+    "Markdown": [".md", ".markdown"],
+    "PHP": [".php", ".php3", ".php4", ".php5", ".phps", ".phpt"],
+    "Perl": [".pl", ".pm", ".pod", ".perl"],
+    "PowerShell": ['.ps1', '.psd1', '.psm1'],
+    "Python": [".py"],
+    "Ruby": [".rb"],
+    "Rust": [".rs"],
+    "SQL": [".sql"],
+    "Scala": [".scala"],
+    "Shell": [".sh", ".bash", ".command", ".zsh"],
+    "TypeScript": [".ts", ".tsx"],
+    "TeX": [".tex"],
+    "Visual Basic": [".vb"]
+}
+```
+
+## Licenses
+Each example is also annotated with the license of the associated repository. There are in total 15 licenses:
+```python
+[
+  'mit',
+  'apache-2.0',
+  'gpl-3.0',
+  'gpl-2.0',
+  'bsd-3-clause',
+  'agpl-3.0',
+  'lgpl-3.0',
+  'lgpl-2.1',
+  'bsd-2-clause',
+  'cc0-1.0',
+  'epl-1.0',
+  'mpl-2.0',
+  'unlicense',
+  'isc',
+  'artistic-2.0'
+ ]
+```
+
+## Dataset Statistics
+
+The dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:
+
+![dataset-statistics](https://huggingface.co/datasets/codeparrot/github-code/resolve/main/github-code-stats-alpha.png)
+
+|    | Language     |File Count| Size (GB)|
+|---:|:-------------|---------:|-------:|
+|  0 | Java         | 19548190 | 107.70 |
+|  1 | C            | 14143113 | 183.83 |
+|  2 | JavaScript   | 11839883 |  87.82 |
+|  3 | HTML         | 11178557 | 118.12 |
+|  4 | PHP          | 11177610 |  61.41 |
+|  5 | Markdown     |  8464626 |  23.09 |
+|  6 | C++          |  7380520 |  87.73 |
+|  7 | Python       |  7226626 |  52.03 |
+|  8 | C#           |  6811652 |  36.83 |
+|  9 | Ruby         |  4473331 |  10.95 |
+| 10 | GO           |  2265436 |  19.28 |
+| 11 | TypeScript   |  1940406 |  24.59 |
+| 12 | CSS          |  1734406 |  22.67 |
+| 13 | Shell        |  1385648 |   3.01 |
+| 14 | Scala        |   835755 |   3.87 |
+| 15 | Makefile     |   679430 |   2.92 |
+| 16 | SQL          |   656671 |   5.67 |
+| 17 | Lua          |   578554 |   2.81 |
+| 18 | Perl         |   497949 |   4.70 |
+| 19 | Dockerfile   |   366505 |   0.71 |
+| 20 | Haskell      |   340623 |   1.85 |
+| 21 | Rust         |   322431 |   2.68 |
+| 22 | TeX          |   251015 |   2.15 |
+| 23 | Batchfile    |   236945 |   0.70 |
+| 24 | CMake        |   175282 |   0.54 |
+| 25 | Visual Basic |   155652 |   1.91 |
+| 26 | FORTRAN      |   142038 |   1.62 |
+| 27 | PowerShell   |   136846 |   0.69 |
+| 28 | Assembly     |    82905 |   0.78 |
+| 29 | Julia        |    58317 |   0.29 |
+
+
+## Dataset Creation
+
+The dataset was created in two steps:
+1. Files of with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery (full query [here](https://huggingface.co/datasets/codeparrot/github-code/blob/main/query.sql)). The query was executed on _Mar 16, 2022, 6:23:39 PM UTC+1_.
+2. Files with lines longer than 1000 characters and duplicates (exact duplicates ignoring whitespaces) were dropped (full preprocessing script [here](https://huggingface.co/datasets/codeparrot/github-code/blob/main/github_preprocessing.py)).
+
+## Considerations for Using the Data
+
+The dataset consists of source code from a wide range of repositories. As such they can potentially include harmful or biased code as well as sensitive information like passwords or usernames.
+
+## Releases
+
+You can load any older version of the dataset with the `revision` argument:
+
+```Python
+ds = load_dataset("codeparrot/github-code", revision="v1.0")
+```
+
+### v1.0
+- Initial release of dataset
+- The query was executed on _Feb 14, 2022, 12:03:16 PM UTC+1_
+
+### v1.1
+- Fix missing Scala/TypeScript
+- Fix deduplication issue with inconsistent Python `hash`
+- The query was executed on _Mar 16, 2022, 6:23:39 PM UTC+1_

huggingface_dataset/Dataset_Card/eu_regulatory_ir.md

@@ -0,0 +1,281 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-retrieval
+task_ids:
+- document-retrieval
+paperswithcode_id: null
+pretty_name: the RegIR datasets
+tags:
+- document-to-document-retrieval
+dataset_info:
+- config_name: eu2uk
+  features:
+  - name: document_id
+    dtype: string
+  - name: publication_year
+    dtype: string
+  - name: text
+    dtype: string
+  - name: relevant_documents
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 20665038
+    num_examples: 1400
+  - name: test
+    num_bytes: 8844145
+    num_examples: 300
+  - name: validation
+    num_bytes: 5852814
+    num_examples: 300
+  - name: uk_corpus
+    num_bytes: 502468359
+    num_examples: 52515
+  download_size: 119685577
+  dataset_size: 537830356
+- config_name: uk2eu
+  features:
+  - name: document_id
+    dtype: string
+  - name: publication_year
+    dtype: string
+  - name: text
+    dtype: string
+  - name: relevant_documents
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 55144655
+    num_examples: 1500
+  - name: test
+    num_bytes: 14810460
+    num_examples: 300
+  - name: validation
+    num_bytes: 15175644
+    num_examples: 300
+  - name: eu_corpus
+    num_bytes: 57212422
+    num_examples: 3930
+  download_size: 31835104
+  dataset_size: 142343181
+---
+
+# Dataset Card for the RegIR datasets
+
+## 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://archive.org/details/eacl2021_regir_datasets
+- **Repository:** https://archive.org/details/eacl2021_regir_datasets
+- **Paper:** https://arxiv.org/abs/2101.10726
+- **Leaderboard:** N/A
+- **Point of Contact:** [Ilias Chalkidis](mailto:ihalk@aueb.gr)
+
+### Dataset Summary
+
+The European Union (EU) has a legislation scheme analogous to regulatory compliance for organizations. According to the Treaty on the Functioning of the European Union (TFEU), all published EU directives must take effect at the national level. Thus, all EU member states must adopt a law to transpose a newly issued directive within the period set by the directive (typically 2 years).
+
+Here, we have two datasets, EU2UK and UK2EU, containing EU directives and UK regulations, which can serve both as queries and documents under the ground truth assumption that a UK law is relevant to the EU directives it transposes and vice versa.
+
+
+
+### Supported Tasks and Leaderboards
+
+The dataset supports:
+
+**EU2UK** (`eu2uk`): Given an EU directive *Q*, retrieve the set of relevant documents from the pool of all available UK regulations. Relevant documents are those that transpose the EU directive (*Q*).
+
+**UK2EU** (`uk2eu`): Given a UK regulation *Q*, retrieve the set of relevant documents from the pool of all available EU directives. Relevant documents are those that are being transposed by the UK regulations (*Q*).
+
+
+### Languages
+
+All documents are written in English.
+
+## Dataset Structure
+
+### Data Instances
+
+```json
+{
+  "document_id": "31977L0794",
+  "publication_year": "1977",
+  "text": "Commission Directive 77/794/EEC ... of agricultural levies and customs duties",
+  "relevant_documents": ["UKPGA19800048", "UKPGA19770036"]
+}
+```
+
+### Data Fields
+
+The following data fields are provided for query documents (`train`, `dev`, `test`):
+
+`document_id`: (**str**) The ID of the document.\
+`publication_year`: (**str**) The publication year of the document.\
+`text`: (**str**) The text of the document.\
+`relevant_documents`: (**List[str]**)  The list of relevant documents, as represented by their `document_id`.
+
+The following data fields are provided for corpus documents (`corpus`):
+
+`document_id`: (**str**) The ID of the document.\
+`publication_year`: (**str**) The publication year of the document.\
+`text`: (**str**) The text of the document.\
+
+### Data Splits
+
+#### EU2UK dataset
+
+| Split         | No of Queries                        | Avg. relevant documents |
+| ------------------- | ------------------------------------  |  --- |
+| Train | 1,400 | 1.79 |
+|Development | 300 | 2.09 |
+|Test | 300  | 1.74 |
+Document Pool (Corpus): 52,515 UK regulations
+
+#### UK2EU dataset
+
+| Split         | No of Queries                         | Avg. relevant documents |
+| ------------------- | ------------------------------------  |  --- |
+| Train | 1,500 | 1.90 |
+|Development | 300 | 1.46 |
+|Test | 300  | 1.29 |
+Document Pool (Corpus): 3,930 EU directives
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was curated by Chalkidis et al. (2021).\
+The transposition pairs are publicly available by the Publications Office of EU (https://publications.europa.eu/en).
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) and Legislation.GOV.UK (http://legislation.gov.uk/) in an unprocessed format.\
+The transposition pairs are provided by the EU member states (in our case, UK) and were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).\
+For more information on the dataset curation, read Chalkidis et al. (2021).
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+* The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) and Legislation.GOV.UK (http://legislation.gov.uk/) in an unprocessed format.
+* The transposition pairs are provided by the EU member states (in our case, UK) and were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).
+
+
+#### Who are the annotators?
+
+Publications Office of EU (https://publications.europa.eu/en)
+
+### Personal and Sensitive Information
+
+The dataset does not include personal or sensitive information.
+
+## 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
+
+Chalkidis et al. (2021)
+
+### Licensing Information
+
+**EU Data**
+
+© European Union, 1998-2021
+
+The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
+
+The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence​​ . This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
+
+Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
+Read more:  https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
+
+**UK Data**
+
+You are encouraged to use and re-use the Information that is available under this licence freely and flexibly, with only a few conditions.
+
+You are free to:
+
+- copy, publish, distribute and transmit the Information;
+- adapt the Information;
+- exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application.
+
+You must (where you do any of the above):
+
+acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/.
+
+### Citation Information
+
+*Ilias Chalkidis, Manos Fergadiotis, Nikos Manginas, Eva Katakalou and Prodromos Malakasiotis.*
+*Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations* 
+*Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021). Online. 2021*
+```
+@inproceedings{chalkidis-etal-2021-regir,
+    title = "Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations",
+    author = "Chalkidis, Ilias  and Fergadiotis, Manos and Manginas, Nikos and Katakalou, Eva,  and Malakasiotis, Prodromos",
+    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)",
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2101.10726",
+}
+```
+
+### Contributions
+
+Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.

huggingface_dataset/Dataset_Card/huggingartists_van-morrison.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/van-morrison"
+
+## 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:** 1.062718 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/2f97270cc1d1420867052a6c331d5820.1000x667x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/van-morrison">
+      <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">Van Morrison</div>
+    <a href="https://genius.com/artists/van-morrison">
+    	<div style="text-align: center; font-size: 14px;">@van-morrison</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/van-morrison).
+
+### 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/van-morrison")
+```
+
+## 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|
+|------:|---------:|---:|
+|929|         -|   -|
+
+'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/van-morrison")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/irds_clueweb12_b13.md

@@ -0,0 +1,35 @@
+---
+pretty_name: '`clueweb12/b13`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `clueweb12/b13`
+
+The `clueweb12/b13` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb12#clueweb12/b13).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=52,343,021
+
+
+This dataset is used by: [`clueweb12_b13_clef-ehealth`](https://huggingface.co/datasets/irds/clueweb12_b13_clef-ehealth), [`clueweb12_b13_clef-ehealth_cs`](https://huggingface.co/datasets/irds/clueweb12_b13_clef-ehealth_cs), [`clueweb12_b13_clef-ehealth_de`](https://huggingface.co/datasets/irds/clueweb12_b13_clef-ehealth_de), [`clueweb12_b13_clef-ehealth_fr`](https://huggingface.co/datasets/irds/clueweb12_b13_clef-ehealth_fr), [`clueweb12_b13_clef-ehealth_hu`](https://huggingface.co/datasets/irds/clueweb12_b13_clef-ehealth_hu), [`clueweb12_b13_clef-ehealth_pl`](https://huggingface.co/datasets/irds/clueweb12_b13_clef-ehealth_pl), [`clueweb12_b13_clef-ehealth_sv`](https://huggingface.co/datasets/irds/clueweb12_b13_clef-ehealth_sv), [`clueweb12_b13_ntcir-www-1`](https://huggingface.co/datasets/irds/clueweb12_b13_ntcir-www-1), [`clueweb12_b13_ntcir-www-2`](https://huggingface.co/datasets/irds/clueweb12_b13_ntcir-www-2), [`clueweb12_b13_ntcir-www-3`](https://huggingface.co/datasets/irds/clueweb12_b13_ntcir-www-3), [`clueweb12_b13_trec-misinfo-2019`](https://huggingface.co/datasets/irds/clueweb12_b13_trec-misinfo-2019)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/clueweb12_b13', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'url': ..., 'date': ..., 'http_headers': ..., 'body': ..., 'body_content_type': ...}
+
+```
+
+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.

huggingface_dataset/Dataset_Card/jacklin_msmarco_passage_ranking_corpus.md

@@ -0,0 +1,3 @@
+This is the preprocessed data from msmarco passage(v1) ranking corpus.
+
+*[MS MARCO: A human generated MAchine Reading COmprehension dataset](https://arxiv.org/pdf/1611.09268.pdf)* SPayal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen,. 

huggingface_dataset/Dataset_Card/jchenyu_t5_large_supervised_proportional_1M.md

@@ -0,0 +1,6 @@
+---
+license: apache-2.0
+---
+This data set is created by randomly sampling 1M documents from [the large supervised proportional mixture](https://github.com/google-research/text-to-text-transfer-transformer/blob/733428af1c961e09ea0b7292ad9ac9e0e001f8a5/t5/data/mixtures.py#L193) from the [T5](https://github.com/google-research/text-to-text-transfer-transformer) repository.
+
+The code to produce this sampled dataset can be found [here](https://github.com/chenyu-jiang/text-to-text-transfer-transformer/blob/main/prepare_dataset.py).

huggingface_dataset/Dataset_Card/jeasinema_SQA3D.md

@@ -0,0 +1,99 @@
+---
+license: cc-by-4.0
+task_categories:
+- question-answering
+tags:
+- 3D vision
+- embodied AI
+size_categories:
+- 10K<n<100K
+---
+
+SQA3D: Situated Question Answering in 3D Scenes (ICLR 2023, https://arxiv.org/abs/2210.07474)
+===
+1. Download the [SQA3D dataset](https://zenodo.org/record/7544818/files/sqa_task.zip?download=1) under `assets/data/`. The following files should be used:
+
+  ```
+  ./assets/data/sqa_task/balanced/*
+  ./assets/data/sqa_task/answer_dict.json
+  ```
+
+2. The dataset has been splited into `train`, `val` and `test`. For each category, we offer both question file, ex. `v1_balanced_questions_train_scannetv2.json`, and annotations, ex. `v1_balanced_sqa_annotations_train_scannetv2.json`
+
+  - The format of question file:
+      Run the following code:
+      ```python
+      import json
+      q = json.load(open('v1_balanced_questions_train_scannetv2.json', 'r'))
+      # Print the total number of questions
+      print('#questions: ', len(q['questions']))
+      print(q['questions'][0])
+      ```
+      The output is:
+      ```json
+      {
+        "alternative_situation": 
+          [
+            "I stand looking out of the window in thought and a radiator is right in front of me.",
+            "I am looking outside through the window behind the desk."
+          ],
+        "question": "What color is the desk to my right?",
+        "question_id": 220602000000,
+        "scene_id": "scene0380_00",
+        "situation": "I am facing a window and there is a desk on my right and a chair behind me."
+      }
+      ```
+      The following fileds are **useful**: `question`, `question_id`, `scene_id`, `situation`.
+      
+  - The format of annotations:
+      Run the following code:
+      ```python
+      import json
+      a = json.load(open('v1_balanced_sqa_annotations_train_scannetv2.json', 'r'))
+      # Print the total number of annotations, should be the same as questions
+      print('#annotations: ', len(a['annotations']))
+      print(a['annotations'][0])
+      ```
+      The output is
+      ```json
+      {
+        "answer_type": "other",
+        "answers": 
+          [
+            {
+              "answer": "brown", 
+              "answer_confidence": "yes", 
+              "answer_id": 1
+            }
+          ],
+        "position": 
+          {
+            "x": -0.9651003385573296,
+            "y": -1.2417634435553606,
+            "z": 0
+          },
+        "question_id": 220602000000,
+        "question_type": "N/A",
+        "rotation": 
+          {
+            "_w": 0.9950041652780182,
+            "_x": 0,
+            "_y": 0,
+            "_z": 0.09983341664682724
+          },
+        "scene_id": "scene0380_00"
+      }
+      ```
+      The following fields are **useful**: `answers[0]['answer']`, `question_id`, `scene_id`.
+      **Note**: To find the answer of a question in the question file, you need to use lookup with `question_id`.
+
+3. We provide the mapping between answers and class labels in `answer_dict.json`
+  ```python
+  import json
+  j = json.load(open('answer_dict.json', 'r'))
+  print('Total classes: ', len(j[0]))
+  print('The class label of answer \'table\' is: ', j[0]['table'])
+  print('The corresponding answer of class 123 is: ', j[1]['123'])
+  ```
+
+4. Loader, model and training code can be found at https://github.com/SilongYong/SQA3D

huggingface_dataset/Dataset_Card/ofis_publik.md

@@ -0,0 +1,165 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- br
+- fr
+license:
+- unknown
+multilinguality:
+- multilingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- translation
+task_ids: []
+paperswithcode_id: null
+pretty_name: OfisPublik
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - br
+        - fr
+  config_name: br-fr
+  splits:
+  - name: train
+    num_bytes: 12256825
+    num_examples: 63422
+  download_size: 3856983
+  dataset_size: 12256825
+---
+
+# Dataset Card for OfisPublik
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** http://opus.nlpl.eu/OfisPublik.php
+- **Repository:** None
+- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
+- **Leaderboard:** [More Information Needed]
+- **Point of Contact:** [More Information Needed]
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

huggingface_dataset/Dataset_Card/vishnun_SpellGram.md

@@ -0,0 +1,110 @@
+---
+license: mit
+task_categories:
+- text2text-generation
+language:
+- en
+tags:
+- NLP
+- Text2Text
+pretty_name: Dataset consisting of grammatical and spelling errors
+size_categories:
+- 10K<n<100K
+---
+# SpellGram
+
+## Dataset consisting of grammatical and spelling errors
+
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[train.csv]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]