Upload batch 325 (20 files, last=huggingface_dataset/Dataset_Card/irds_mr-tydi_bn.md)

huggingface_dataset/Dataset_Card/3ee_regularization-castle.md

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+---
+license: mit
+tags:
+- stable-diffusion
+- regularization-images
+- text-to-image
+- image-to-image
+- dreambooth
+- class-instance
+- preservation-loss-training 
+---
+
+# Castle Regularization Images
+
+A collection of regularization & class instance datasets of castles for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training.

huggingface_dataset/Dataset_Card/BSC-LT_sts-ca.md

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+---
+language:
+- ca
+---
+# Semantic Textual Similarity in Catalan
+
+## BibTeX  citation
+
+If you use any of these resources (datasets or models) in your work, please cite our latest paper:
+
+```bibtex
+@inproceedings{armengol-estape-etal-2021-multilingual,
+    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
+    author = "Armengol-Estap{\'e}, Jordi  and
+      Carrino, Casimiro Pio  and
+      Rodriguez-Penagos, Carlos  and
+      de Gibert Bonet, Ona  and
+      Armentano-Oller, Carme  and
+      Gonzalez-Agirre, Aitor  and
+      Melero, Maite  and
+      Villegas, Marta",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.437",
+    doi = "10.18653/v1/2021.findings-acl.437",
+    pages = "4933--4946",
+}
+```
+
+
+## Digital Object Identifier (DOI) and access to dataset files
+
+https://doi.org/10.5281/zenodo.4529184
+
+
+## Introduction
+
+STS corpus is a benchmark for evaluating Semantic Text Similarity in Catalan.
+It consists of more than 3000 sentence pairs, annotated with the semantic similarity between them, using a scale from 0 (no similarity at all) to 5 (semantic equivalence). It is done manually by 4 different annotators following our guidelines based on previous work from the SemEval challenges (https://www.aclweb.org/anthology/S13-1004.pdf).
+
+The source data are scraped sentences from the Catalan Textual Corpus (https://doi.org/10.5281/zenodo.4519349), used under CC-by-SA-4.0 licence (https://creativecommons.org/licenses/by-sa/4.0/). The dataset is released under the same licence.
+
+This dataset was developed by BSC TeMU as part of the AINA project, to enrich the Catalan Language Understanding Benchmark (CLUB). 
+
+This is the version 1.0.1 of the dataset with the complete human and automatic annotations, as well as the QA analysis scripts. It also has a more accurate license.
+
+This dataset can be used to build and score semantic similarity models.
+
+### Supported Tasks and Leaderboards
+
+Semantic textual similiarity, Language Model
+
+### Languages
+
+CA - Catalan
+
+### Directory structure
+
+    * dev.tsv
+    * sts-ca.py
+    * test.tsv
+    * train.tsv
+    * README
+   
+## Dataset Structure
+
+### Data Instances
+
+Follows SemEval challenges (https://www.aclweb.org/anthology/S13-1004.pdf).
+
+### Data Fields
+
+SemEval challenges formats and conventions (https://www.aclweb.org/anthology/S13-1004.pdf).
+
+### Example:
+ |  index  |  id  |  sentence 1  |  sentence 2  |  avg  | 
+ | ------- | ---- | ------------ | ------------ | ----- | 
+| 19 | ACN2_131 | Els manifestants ocupen l'Imperial Tarraco durant una hora fent jocs de taula | Els manifestants ocupen l'Imperial Tarraco i fan jocs de taula | 4 |
+| 21 | TE2_80 | El festival comptarà amb cinc escenaris i se celebrarà entre el 7 i el 9 de juliol al Parc del Fòrum. | El festival se celebrarà el 7 i 8 de juliol al Parc del Fòrum de Barcelona | 3 |
+| 23 | Oscar2_609 | Aleshores hi posarem un got de vi i continuarem amb la cocció fins que s'hagi evaporat el vi i ho salpebrarem. | Mentre, hi posarem el vi al sofregit i deixarem coure uns 7/8′, fins que el vi s'evapori. | 3 |
+| 25 | Viqui2_48 | L'arboç grec (Arbutus andrachne) és un arbust o un petit arbre dins la família ericàcia. | El ginjoler ("Ziziphus jujuba") és un arbust o arbre petit de la família de les "Rhamnaceae". | 2.75 |
+| 27 | ACN2_1072 | Mentre han estat davant la comandància, els manifestants han cridat consignes a favor de la independència i han cantat cançons com 'L'estaca'. | Entre les consignes que han cridat s'ha pogut escoltar càntics com 'els carrers seran sempre nostres' i contínues consignes en favor de la independència. | 3 |
+| 28 | Viqui2_587 | Els cinc municipis ocupen una superfície de poc més de 100 km2 i conjuntament sumen una població total aproximada de 3.691 habitants (any 2019). | Té una població d'1.811.177 habitants (2005) repartits en 104 municipis d'una superfície total de 14.001 km2. | 2.67 |
+
+### Data Splits
+* sts_cat_dev_v1.tsv (493 annotated pairs)
+* sts_cat_train_v1.tsv (492 annotated pairs)
+* sts_cat_test_v1.tsv (2043 annotated pairs)
+
+
+## Dataset Creation
+
+### Methodology
+
+Random sentences were extracted from 3 Catalan corpus: ACN, Oscar and Wikipedia, and we generated candidate pairs using a combination of metrics from Doc2Vec, Jaccard and a BERT-like model (“distiluse-base-multilingual-cased-v2”, [link](https://huggingface.co/distilbert-base-multilingual-cased)). Finally, we  manually reviewed the generated pairs to reject non-relevant pairs (identical or ungrammatical sentences, etc.) before providing them to the annotation team.
+The average of the four annotations was selected as a “ground truth” for each sentence pair, except when an annotator diverged in more than one unit from the average. In these cases, we discarded the divergent annotation and recalculated the average without it. We also discarded 45 sentence pairs because the annotators disagreed too much.
+
+### Curation Rationale
+
+For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The source data are scraped sentences from the Catalan Textual Corpus.
+
+#### Who are the source language producers?
+
+The Catalan Textual Corpus is a 1760-million-token web corpus of Catalan built from several sources: existing corpus such as DOGC, CaWac (non-dedup version), Oscar (unshuffled version), Open Subtitles, Catalan Wikipedia; and three brand new crawlings: the Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains; the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government; and the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the Catalan News Agency.
+
+### Annotations
+
+#### Annotation process
+
+We comissioned the manual annotation of the similiarity between the sentences of each pair, following the provided guidelines.
+
+#### Who are the annotators?
+
+A team of native language speakers from 2 different companies, working independently.
+
+### Dataset Curators
+
+Carlos Rodríguez and Carme Armentano, from BSC-CNS
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+
+## Contact
+
+Carlos Rodríguez-Penagos or Carme Armentano-Oller (bsc-temu@bsc.es)
+
+
+## License
+
+<a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/"><img alt="Attribution-ShareAlike 4.0 International License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>.

huggingface_dataset/Dataset_Card/Jerimee_autotrain-data-dontknowwhatImdoing.md

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+---
+language:
+- en
+task_categories:
+- text-classification
+---
+# AutoTrain Dataset for project: dontknowwhatImdoing
+
+## Dataset Descritpion
+
+This dataset has been automatically processed by AutoTrain for project dontknowwhatImdoing.
+
+### 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
+[
+  {
+    "text": "Gaston",
+    "target": 1
+  },
+  {
+    "text": "Churchundyr",
+    "target": 0
+  }
+]
+```
+
+Note that, sadly, it flipped the boolean, using 1 for mundane and 0 for goblin.
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "text": "Value(dtype='string', id=None)",
+  "target": "ClassLabel(num_classes=2, names=['Goblin', 'Mundane'], 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        | 965 |
+| valid        | 242 |

huggingface_dataset/Dataset_Card/NLPC-UOM_Sinhala-News-Category-classification.md

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+---
+annotations_creators: []
+language_creators:
+- crowdsourced
+language:
+- si
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: sinhala-news-category-classification
+size_categories:
+- 1K<n<10K
+source_datasets: []
+task_categories:
+- text-classification
+task_ids: []
+---
+
+
+This file contains news texts (sentences) belonging to 5 different news categories (political, business, technology, sports and Entertainment). The original dataset was released by Nisansa de Silva (*Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language, 2015*). The original dataset is processed and cleaned of single word texts, English only sentences etc. 
+
+If you use this dataset, please cite {*Nisansa de Silva, Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language, 2015*} and {*Dhananjaya et al. BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, 2022*}

huggingface_dataset/Dataset_Card/SetFit_subj.md

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+# Subjective vs Objective
+
+This is the SUBJ dataset as used in [SentEval](https://github.com/facebookresearch/SentEval). It contains sentences with an annotation if they sentence describes something subjective about a movie or something objective

huggingface_dataset/Dataset_Card/TurkuNLP_turku_paraphrase_corpus.md

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+---
+YAML tags:
+annotations_creators:
+- expert-generated
+language_creators: []
+language:
+- fi
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: Turku Paraphrase Corpus
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+- sentence-similarity
+- text2text-generation
+- other
+task_ids:
+- semantic-similarity-classification
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://turkunlp.org/paraphrase.html
+- **Repository:** https://github.com/TurkuNLP/Turku-paraphrase-corpus
+- **Paper:** https://aclanthology.org/2021.nodalida-main.29
+- **Leaderboard:** Not available
+- **Point of Contact:** [Jenna Kanerva, Filip Ginter](mailto:jmnybl@utu.fi,filip.ginter@gmail.com)
+
+### Dataset Summary
+
+The project gathered a large dataset of Finnish paraphrase pairs (over 100,000). The paraphrases are selected and classified manually, so as to minimize lexical overlap, and provide examples that are maximally structurally and lexically different. The objective is to create a dataset which is challenging and better tests the capabilities of natural language understanding. An important feature of the data is that most paraphrase pairs are distributed in their document context. The primary application for the dataset is the development and evaluation of deep language models, and representation learning in general.
+
+Usage:
+```
+  from datasets import load_dataset
+  dataset = load_dataset('TurkuNLP/turku_paraphrase_corpus', name="plain")
+```
+where `name` is one of the supported loading options: `plain`, `plain-context`, `classification`, `classification-context`, or `generation`. See Data Fields for more information. 
+
+### Supported Tasks and Leaderboards
+
+* Paraphrase classification
+* Paraphrase generation
+
+### Languages
+
+Finnish
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example includes two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset includes three different configurations: `plain`, `classification`, and `generation`. The `plain` configuration loads the original data without any additional preprocessing or transformations, while the `classification` configuration directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` configuration, the examples are preprocessed to be directly suitable for the paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).
+
+Each pair in the `plain` and `classification` configurations will include fields:
+
+`id`: 
+Identifier of the paraphrase pair (string)
+
+`gem_id`:
+Identifier of the paraphrase pair in the GEM dataset (string)
+
+`goeswith`: 
+Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data. All examples with the same `goeswith` value (other than `not available`) should be kept together in any train/dev/test split; most users won't need this  (string)
+
+`fold`: 
+0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold, most users won't need this (int)
+
+`text1`: 
+First paraphrase passage (string)
+
+`text2`: 
+Second paraphrase passage (string)
+
+`label`: 
+Manually annotated labels (string)
+
+`binary_label`: 
+Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string)
+
+`is_rewrite`: 
+Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)
+
+Each pair in the `generation` config will include the same fields except `text1` and `text2` are renamed to `input` and `output` in order to indicate the generation direction. Thus the fields are: `id`, `gem_id`, `goeswith`, `fold`, `input`, `output`, `label`, `binary_label`, and `is_rewrite`
+
+**Context**: Most (but not all) of the paraphrase pairs are identified in their document context. By default, these contexts are not included to conserve memory, but can be accessed using the configurations `plain-context` and `classification-context`. These are exactly like `plain` and `classification` with these additional fields:
+
+`context1`: 
+a dictionary with the fields `doctext` (string), `begin` (int), `end` (int). These mean that the paraphrase in `text1` was extracted from `doctext[begin:end]`. In most cases, `doctext[begin:end]` and `text1` are the exact same string, but occassionally that is not the case when e.g. intervening punctuations or other unrelated texts were "cleaned" from `text1` during annotation. In case the context is not available, `doctext` is an empty string and `beg==end==0`
+
+`context2`: 
+same as `context1` but for `text2`
+
+### 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
+
+Thanks to [@jmnybl](https://github.com/jmnybl) and [@fginter](https://github.com/fginter) for adding this dataset.

huggingface_dataset/Dataset_Card/UKPLab_TexPrax.md

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+---
+license: cc-by-nc-4.0
+---
+# Dataset Card for TexPrax
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage: https://texprax.de/**
+- **Repository: https://github.com/UKPLab/TexPrax**
+- **Paper: https://arxiv.org/abs/2208.07846**
+- **Leaderboard: n/a**
+- **Point of Contact: Ji-Ung Lee (http://www.ukp.tu-darmstadt.de/)**
+
+### Dataset Summary
+
+This dataset contains dialogues collected from German factory workers at the _Center for industrial productivity_ ([CiP](https://www.prozesslernfabrik.de/)). The  dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.
+
+### Supported Tasks and Leaderboards
+
+This dataset supports the following tasks:
+
+* Sentence classification
+* Named entity recognition (will be updated soon with the new indexing)
+* Dialog generation (so far not evaluated)
+
+### Languages
+
+German
+
+## Dataset Structure
+
+### Data Instances
+
+On sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit. 
+
+```
+{"185";"562";993";"wie kriege ich die Dichtung raus?";"P";"n/a";"3"}
+```
+
+On token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.
+```
+{"178_0";"['Hi', 'wie', 'kriege', 'ich', 'die', 'Dichtung', 'raus', '?', 'in', 'der', 'Schublade', 'gibt', 'es', 'einen', 'Dichtungszieher']";"['O', 'O', 'O', 'O', 'O', 'B-PRE', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'B-PE']";"Batch 3"}
+```
+
+### Data Fields
+
+Sentence level: 
+
+* dialog-id: unique identifier for the dialog
+* turn-id: unique identifier for the turn
+* sentence-id: unique identifier for the dialog
+* sentence: the respective sentence
+* label: the label (_P_ for Problem, _C_ for Cause, _S_ for solution, and _O_ for Other)
+* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).
+* subsplit: the respective subsplit of the data (see below)
+
+Token level: 
+
+* id: the identifier
+* tokens: a list of tokens (i.e., the tokenized dialogue) 
+* entities: the named entity in a BIO scheme (_B-X_, _I-X_, or O). 
+* subsplit: the respective subsplit of the data (see below)
+
+
+### Data Splits
+
+The dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory. 
+
+Train:
+* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line
+* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line
+* Batch 2: data collected in-between October 2021-June 2022 from all workers
+
+Test:
+* Batch 3: data collected in July 2022 together with the system usability study run
+
+Sentence level statistics: 
+
+| Batch  | Dialogues | Turns | Sentences |   
+|---|---|---|---|
+| 1  |  81 | 246  | 553  |   
+| 2 |  97 | 309  | 432  |   
+| 3  |  24 | 36  | 42  |   
+| Overall | 202 | 591 | 1,027 |   
+
+Token level statistics: 
+[Needs to be added]
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset provides task-oriented dialogues that solve a very domain specific problem.  
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data was generated by workers at the [CiP](https://www.prozesslernfabrik.de/). The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the [paper](https://arxiv.org/abs/2208.07846).
+
+#### Who are the source language producers?
+
+German factory workers working at the [CiP](https://www.prozesslernfabrik.de/)
+
+### Annotations
+
+#### Annotation process
+
+**Token level.** Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.
+
+**Sentence level.** Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the [TexPrax demo paper](https://arxiv.org/abs/2208.07846).
+
+#### Who are the annotators?
+
+**Token level.**  Researchers working at the CiP.
+
+**Sentence level.** The factory workers themselves.
+
+### Personal and Sensitive Information
+
+This dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+Informal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold. 
+
+### Discussion of Biases
+
+The dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines). 
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+You can download the data via:
+
+```
+from datasets import load_dataset
+
+dataset = load_dataset("UKPLab/TexPrax") # default config is sentence classification
+dataset = load_dataset("UKPLab/TexPrax", "ner") # use the ner tag for named entity recognition
+``` 
+Please find more information about the code and how the data was collected on [GitHub](https://github.com/UKPLab/TexPrax).
+
+### Dataset Curators
+
+Curation is managed by our [data manager](https://www.informatik.tu-darmstadt.de/ukp/research_ukp/ukp_research_data_and_software/ukp_data_and_software.en.jsp) at UKP.
+
+### Licensing Information
+
+[CC-by-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
+
+### Citation Information
+
+Please cite this data using: 
+
+```
+@article{stangier2022texprax,
+  title={TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation},
+  author={Stangier, Lorenz and Lee, Ji-Ung and Wang, Yuxi and M{\"u}ller, Marvin and Frick, Nicholas and Metternich, Joachim and Gurevych, Iryna},
+  journal={arXiv preprint arXiv:2208.07846},
+  year={2022}
+}
+```
+
+### Contributions
+
+Thanks to [@Wuhn](https://github.com/Wuhn) for adding this dataset.
+
+## Tags
+
+annotations_creators:
+- expert-generated
+
+language:
+- de
+
+language_creators:
+- expert-generated
+
+license:
+- cc-by-nc-4.0
+
+multilinguality:
+- monolingual
+
+pretty_name: TexPrax-Conversations
+
+size_categories:
+- n<1K
+- 1K<n<10K
+
+source_datasets:
+- original
+
+tags:
+- dialog
+- expert to expert conversations
+- task-oriented
+
+task_categories:
+- token-classification
+- text-classification
+
+task_ids:
+- named-entity-recognition
+- multi-class-classification

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-billsum-default-a34c3f-1465353966.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- billsum
+eval_info:
+  task: summarization
+  model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
+  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-booksum-V12
+* 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/autoevaluate_autoeval-eval-futin__feed-sen_en-2f01d7-2175769986.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/feed
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-30b
+  metrics: []
+  dataset_name: futin/feed
+  dataset_config: sen_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: facebook/opt-30b
+* Dataset: futin/feed
+* Config: sen_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-inverse-scaling__NeQA-inverse-scaling__NeQA-1e740e-1694759585.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- inverse-scaling/NeQA
+eval_info:
+  task: text_zero_shot_classification
+  model: inverse-scaling/opt-2.7b_eval
+  metrics: []
+  dataset_name: inverse-scaling/NeQA
+  dataset_config: inverse-scaling--NeQA
+  dataset_split: train
+  col_mapping:
+    text: prompt
+    classes: classes
+    target: answer_index
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: inverse-scaling/opt-2.7b_eval
+* Dataset: inverse-scaling/NeQA
+* Config: inverse-scaling--NeQA
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-jeffdshen__redefine_math_test0-jeffdshen__redefine_math-58f952-1666158903.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- jeffdshen/redefine_math_test0
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-30b
+  metrics: []
+  dataset_name: jeffdshen/redefine_math_test0
+  dataset_config: jeffdshen--redefine_math_test0
+  dataset_split: train
+  col_mapping:
+    text: prompt
+    classes: classes
+    target: answer_index
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: facebook/opt-30b
+* Dataset: jeffdshen/redefine_math_test0
+* Config: jeffdshen--redefine_math_test0
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-samsum-0c672345-10275366.md

@@ -0,0 +1,31 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- samsum
+eval_info:
+  task: summarization
+  model: knkarthick/bart-large-xsum-samsum
+  metrics: []
+  dataset_name: samsum
+  dataset_config: samsum
+  dataset_split: train
+  col_mapping:
+    text: dialogue
+    target: summary
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: knkarthick/bart-large-xsum-samsum
+* Dataset: samsum
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@ikadebi](https://huggingface.co/ikadebi) for evaluating this model.

huggingface_dataset/Dataset_Card/huggingartists_the-the-pigs.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/the-the-pigs"
+
+## 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:** 0.077582 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/2f1fd1b951237ad3387096f392d41fa5.720x720x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/the-the-pigs">
+      <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">The ‘’Вепри’’ (The Pigs)</div>
+    <a href="https://genius.com/artists/the-the-pigs">
+    	<div style="text-align: center; font-size: 14px;">@the-the-pigs</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/the-the-pigs).
+
+### 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/the-the-pigs")
+```
+
+## 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|
+|------:|---------:|---:|
+|28|         -|   -|
+
+'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/the-the-pigs")
+
+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_tom-waits.md

@@ -0,0 +1,198 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/tom-waits"
+
+## 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:** 0.818237 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/505d2d5d1d43304dca446fd2e788a0f8.750x750x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/tom-waits">
+      <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">Tom Waits</div>
+    <a href="https://genius.com/artists/tom-waits">
+    	<div style="text-align: center; font-size: 14px;">@tom-waits</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/tom-waits).
+
+### 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/tom-waits")
+```
+
+## 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|
+|------:|---------:|---:|
+|681|         -|   -|
+
+'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/tom-waits")
+
+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)
+
+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_young-thug.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/young-thug"
+
+## 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:** 4.254273 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/b08755976e2dcad78a75ee47059adcbc.777x777x1.png&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/young-thug">
+      <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">Young Thug</div>
+    <a href="https://genius.com/artists/young-thug">
+    	<div style="text-align: center; font-size: 14px;">@young-thug</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/young-thug).
+
+### 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/young-thug")
+```
+
+## 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|
+|------:|---------:|---:|
+|1656|         -|   -|
+
+'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/young-thug")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/irds_mr-tydi_bn.md

@@ -0,0 +1,62 @@
+---
+pretty_name: '`mr-tydi/bn`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mr-tydi/bn`
+
+The `mr-tydi/bn` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/bn).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=304,059
+ - `queries` (i.e., topics); count=2,264
+ - `qrels`: (relevance assessments); count=2,292
+
+
+This dataset is used by: [`mr-tydi_bn_dev`](https://huggingface.co/datasets/irds/mr-tydi_bn_dev), [`mr-tydi_bn_test`](https://huggingface.co/datasets/irds/mr-tydi_bn_test), [`mr-tydi_bn_train`](https://huggingface.co/datasets/irds/mr-tydi_bn_train)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/mr-tydi_bn', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ...}
+
+queries = load_dataset('irds/mr-tydi_bn', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mr-tydi_bn', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@article{Zhang2021MrTyDi,
+  title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, 
+  author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
+  year={2021},
+  journal={arXiv:2108.08787},
+}
+@article{Clark2020TyDiQa,
+    title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
+    author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
+    year={2020},
+    journal={Transactions of the Association for Computational Linguistics}
+}
+```

huggingface_dataset/Dataset_Card/mteb_emotion.md

@@ -0,0 +1,6 @@
+---
+language:
+- en
+---
+
+** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)**

huggingface_dataset/Dataset_Card/nateraw_airbnb-stock-price-new.md

@@ -0,0 +1,130 @@
+---
+license:
+- cc0-1.0
+kaggle_id: evangower/airbnb-stock-price
+---
+
+# Dataset Card for Airbnb Stock Price
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://kaggle.com/datasets/evangower/airbnb-stock-price
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+This contains the historical stock price of Airbnb (ticker symbol ABNB) an American company that operates an online marketplace for lodging, primarily homestays for vacation rentals, and tourism activities. Based in San Francisco, California, the platform is accessible via website and mobile app. 
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+This dataset was shared by [@evangower](https://kaggle.com/evangower)
+
+### Licensing Information
+
+The license for this dataset is cc0-1.0
+
+### Citation Information
+
+```bibtex
+[More Information Needed]
+```
+
+### Contributions
+
+[More Information Needed]

huggingface_dataset/Dataset_Card/pysentimiento_spanish-tweets.md

@@ -0,0 +1,99 @@
+---
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: tweet_id
+    dtype: string
+  - name: user_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 82649695458
+    num_examples: 597433111
+  - name: test
+    num_bytes: 892219251
+    num_examples: 6224733
+  download_size: 51737237106
+  dataset_size: 83541914709
+---
+
+# spanish-tweets
+
+## A big corpus of tweets for pretraining embeddings and language models
+
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage**: https://github.com/pysentimiento/robertuito
+- **Paper**: [RoBERTuito: a pre-trained language model for social media text in Spanish](https://aclanthology.org/2022.lrec-1.785/)
+- **Point of Contact:** jmperez (at) dc.uba.ar
+
+### Dataset Summary
+
+A big dataset of (mostly) Spanish tweets for pre-training language models (or other representations).
+
+### Supported Tasks and Leaderboards
+
+Language Modeling
+
+### Languages
+
+Mostly Spanish, but some Portuguese, English, and other languages.
+
+## Dataset Structure
+
+
+### Data Fields
+
+- *tweet_id*: id of the tweet
+- *user_id*: id of the user
+- *text*: text from the tweet
+
+## Dataset Creation
+
+The full process of data collection is described in the paper. Here we roughly outline the main points:
+
+- A Spritzer collection uploaded to Archive.org dating from May 2019 was downloaded
+- From this, we only kept tweets with language metadata equal to Spanish, and mark the users who posted these messages.
+- Then, the tweetline from each of these marked users was downloaded.
+
+
+This corpus consists of 622M tweets from around 432K users.
+
+Please note that we did not filter tweets from other languages, so you might find English, Portuguese, Catalan and other languages in the dataset (around 7/8% of the tweets are not in Spanish)
+
+### Citation Information
+
+```
+@inproceedings{perez-etal-2022-robertuito,
+    title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish",
+    author = "P{\'e}rez, Juan Manuel  and
+      Furman, Dami{\'a}n Ariel  and
+      Alonso Alemany, Laura  and
+      Luque, Franco M.",
+    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
+    month = jun,
+    year = "2022",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2022.lrec-1.785",
+    pages = "7235--7243",
+    abstract = "Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.",
+}
+```

huggingface_dataset/Dataset_Card/selqa.md

@@ -0,0 +1,473 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- en
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+paperswithcode_id: selqa
+pretty_name: SelQA
+dataset_info:
+- config_name: answer_selection_analysis
+  features:
+  - name: section
+    dtype: string
+  - name: question
+    dtype: string
+  - name: article
+    dtype: string
+  - name: is_paraphrase
+    dtype: bool
+  - name: topic
+    dtype:
+      class_label:
+        names:
+          '0': MUSIC
+          '1': TV
+          '2': TRAVEL
+          '3': ART
+          '4': SPORT
+          '5': COUNTRY
+          '6': MOVIES
+          '7': HISTORICAL EVENTS
+          '8': SCIENCE
+          '9': FOOD
+  - name: answers
+    sequence: int32
+  - name: candidates
+    sequence: string
+  - name: q_types
+    sequence:
+      class_label:
+        names:
+          '0': what
+          '1': why
+          '2': when
+          '3': who
+          '4': where
+          '5': how
+          '6': ''
+  splits:
+  - name: train
+    num_bytes: 9676758
+    num_examples: 5529
+  - name: test
+    num_bytes: 2798537
+    num_examples: 1590
+  - name: validation
+    num_bytes: 1378407
+    num_examples: 785
+  download_size: 14773444
+  dataset_size: 13853702
+- config_name: answer_selection_experiments
+  features:
+  - name: question
+    dtype: string
+  - name: candidate
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': '0'
+          '1': '1'
+  splits:
+  - name: train
+    num_bytes: 13782826
+    num_examples: 66438
+  - name: test
+    num_bytes: 4008077
+    num_examples: 19435
+  - name: validation
+    num_bytes: 1954877
+    num_examples: 9377
+  download_size: 18602700
+  dataset_size: 19745780
+- config_name: answer_triggering_analysis
+  features:
+  - name: section
+    dtype: string
+  - name: question
+    dtype: string
+  - name: article
+    dtype: string
+  - name: is_paraphrase
+    dtype: bool
+  - name: topic
+    dtype:
+      class_label:
+        names:
+          '0': MUSIC
+          '1': TV
+          '2': TRAVEL
+          '3': ART
+          '4': SPORT
+          '5': COUNTRY
+          '6': MOVIES
+          '7': HISTORICAL EVENTS
+          '8': SCIENCE
+          '9': FOOD
+  - name: q_types
+    sequence:
+      class_label:
+        names:
+          '0': what
+          '1': why
+          '2': when
+          '3': who
+          '4': where
+          '5': how
+          '6': ''
+  - name: candidate_list
+    sequence:
+    - name: article
+      dtype: string
+    - name: section
+      dtype: string
+    - name: candidates
+      sequence: string
+    - name: answers
+      sequence: int32
+  splits:
+  - name: train
+    num_bytes: 30176650
+    num_examples: 5529
+  - name: test
+    num_bytes: 8766787
+    num_examples: 1590
+  - name: validation
+    num_bytes: 4270904
+    num_examples: 785
+  download_size: 46149676
+  dataset_size: 43214341
+- config_name: answer_triggering_experiments
+  features:
+  - name: question
+    dtype: string
+  - name: candidate
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': '0'
+          '1': '1'
+  splits:
+  - name: train
+    num_bytes: 42956518
+    num_examples: 205075
+  - name: test
+    num_bytes: 12504961
+    num_examples: 59845
+  - name: validation
+    num_bytes: 6055616
+    num_examples: 28798
+  download_size: 57992239
+  dataset_size: 61517095
+---
+
+# Dataset Card for SelQA
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/emorynlp/selqa
+- **Repository:** https://github.com/emorynlp/selqa
+- **Paper:** https://arxiv.org/abs/1606.00851
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** Tomasz Jurczyk <http://tomaszjurczyk.com/>, Jinho D. Choi <http://www.mathcs.emory.edu/~choi/home.html>
+
+### Dataset Summary
+
+SelQA: A New Benchmark for Selection-Based Question Answering
+
+
+### Supported Tasks and Leaderboards
+
+Question Answering
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+An example from the `answer selection` set:
+```
+{
+        "section": "Museums",
+        "question": "Where are Rockefeller Museum and LA Mayer Institute for Islamic Art?",
+        "article": "Israel",
+        "is_paraphrase": true,
+        "topic": "COUNTRY",
+        "answers": [
+            5
+        ],
+        "candidates": [
+            "The Israel Museum in Jerusalem is one of Israel's most important cultural institutions and houses the Dead Sea scrolls, along with an extensive collection of Judaica and European art.",
+            "Israel's national Holocaust museum, Yad Vashem, is the world central archive of Holocaust-related information.",
+            "Beth Hatefutsoth (the Diaspora Museum), on the campus of Tel Aviv University, is an interactive museum devoted to the history of Jewish communities around the world.",
+            "Apart from the major museums in large cities, there are high-quality artspaces in many towns and \"kibbutzim\".",
+            "\"Mishkan Le'Omanut\" on Kibbutz Ein Harod Meuhad is the largest art museum in the north of the country.",
+            "Several Israeli museums are devoted to Islamic culture, including the Rockefeller Museum and the L. A. Mayer Institute for Islamic Art, both in Jerusalem.",
+            "The Rockefeller specializes in archaeological remains from the Ottoman and other periods of Middle East history.",
+            "It is also the home of the first hominid fossil skull found in Western Asia called Galilee Man.",
+            "A cast of the skull is on display at the Israel Museum."
+        ],
+        "q_types": [
+            "where"
+        ]
+    }
+```
+
+An example from the `answer triggering` set:
+```
+{
+        "section": "Museums",
+        "question": "Where are Rockefeller Museum and LA Mayer Institute for Islamic Art?",
+        "article": "Israel",
+        "is_paraphrase": true,
+        "topic": "COUNTRY",
+        "candidate_list": [
+            {
+                "article": "List of places in Jerusalem",
+                "section": "List_of_places_in_Jerusalem-Museums",
+                "answers": [],
+                "candidates": [
+                    " Israel Museum *Shrine of the Book *Rockefeller Museum of Archeology Bible Lands Museum Jerusalem Yad Vashem Holocaust Museum L.A. Mayer Institute for Islamic Art Bloomfield Science Museum Natural History Museum Museum of Italian Jewish Art Ticho House Tower of David Jerusalem Tax Museum Herzl Museum Siebenberg House Museums.",
+                    "Museum on the Seam "
+                ]
+            },
+            {
+                "article": "Israel",
+                "section": "Israel-Museums",
+                "answers": [
+                    5
+                ],
+                "candidates": [
+                    "The Israel Museum in Jerusalem is one of Israel's most important cultural institutions and houses the Dead Sea scrolls, along with an extensive collection of Judaica and European art.",
+                    "Israel's national Holocaust museum, Yad Vashem, is the world central archive of Holocaust-related information.",
+                    "Beth Hatefutsoth (the Diaspora Museum), on the campus of Tel Aviv University, is an interactive museum devoted to the history of Jewish communities around the world.",
+                    "Apart from the major museums in large cities, there are high-quality artspaces in many towns and \"kibbutzim\".",
+                    "\"Mishkan Le'Omanut\" on Kibbutz Ein Harod Meuhad is the largest art museum in the north of the country.",
+                    "Several Israeli museums are devoted to Islamic culture, including the Rockefeller Museum and the L. A. Mayer Institute for Islamic Art, both in Jerusalem.",
+                    "The Rockefeller specializes in archaeological remains from the Ottoman and other periods of Middle East history.",
+                    "It is also the home of the first hominid fossil skull found in Western Asia called Galilee Man.",
+                    "A cast of the skull is on display at the Israel Museum."
+                ]
+            },
+            {
+                "article": "L. A. Mayer Institute for Islamic Art",
+                "section": "L._A._Mayer_Institute_for_Islamic_Art-Abstract",
+                "answers": [],
+                "candidates": [
+                    "The L.A. Mayer Institute for Islamic Art (Hebrew: \u05de\u05d5\u05d6\u05d9\u05d0\u05d5\u05df \u05dc.",
+                    "\u05d0.",
+                    "\u05de\u05d0\u05d9\u05e8 \u05dc\u05d0\u05de\u05e0\u05d5\u05ea \u05d4\u05d0\u05e1\u05dc\u05d0\u05dd) is a museum in Jerusalem, Israel, established in 1974.",
+                    "It is located in Katamon, down the road from the Jerusalem Theater.",
+                    "The museum houses Islamic pottery, textiles, jewelry, ceremonial objects and other Islamic cultural artifacts.",
+                    "It is not to be confused with the Islamic Museum, Jerusalem. "
+                ]
+            },
+            {
+                "article": "Islamic Museum, Jerusalem",
+                "section": "Islamic_Museum,_Jerusalem-Abstract",
+                "answers": [],
+                "candidates": [
+                    "The Islamic Museum is a museum on the Temple Mount in the Old City section of Jerusalem.",
+                    "On display are exhibits from ten periods of Islamic history encompassing several Muslim regions.",
+                    "The museum is located adjacent to al-Aqsa Mosque.",
+                    "It is not to be confused with the L. A. Mayer Institute for Islamic Art, also a museum in Jerusalem. "
+                ]
+            },
+            {
+                "article": "L. A. Mayer Institute for Islamic Art",
+                "section": "L._A._Mayer_Institute_for_Islamic_Art-Contemporary_Arab_art",
+                "answers": [],
+                "candidates": [
+                    "In 2008, a group exhibit of contemporary Arab art opened at L.A. Mayer Institute, the first show of local Arab art in an Israeli museum and the first to be mounted by an Arab curator.",
+                    "Thirteen Arab artists participated in the show. "
+                ]
+            }
+        ],
+        "q_types": [
+            "where"
+        ]
+    }
+```
+
+An example from any of the `experiments` data:
+```
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	The Israel Museum in Jerusalem is one of Israel 's most important cultural institutions and houses the Dead Sea scrolls , along with an extensive collection of Judaica and European art .	0
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	Israel 's national Holocaust museum , Yad Vashem , is the world central archive of Holocaust - related information .	0
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	Beth Hatefutsoth ( the Diaspora Museum ) , on the campus of Tel Aviv University , is an interactive museum devoted to the history of Jewish communities around the world .	0
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	Apart from the major museums in large cities , there are high - quality artspaces in many towns and " kibbutzim " .	0
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	" Mishkan Le'Omanut " on Kibbutz Ein Harod Meuhad is the largest art museum in the north of the country .	0
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	Several Israeli museums are devoted to Islamic culture , including the Rockefeller Museum and the L. A. Mayer Institute for Islamic Art , both in Jerusalem .	1
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	The Rockefeller specializes in archaeological remains from the Ottoman and other periods of Middle East history .	0
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	It is also the home of the first hominid fossil skull found in Western Asia called Galilee Man .	0
+Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ?	A cast of the skull is on display at the Israel Museum .	0
+```
+
+### Data Fields
+
+#### Answer Selection
+##### Data for Analysis
+
+for analysis, the columns are: 
+
+* `question`: the question.
+* `article`: the Wikipedia article related to this question.
+* `section`: the section in the Wikipedia article related to this question.
+* `topic`: the topic of this question, where the topics are *MUSIC*, *TV*, *TRAVEL*, *ART*, *SPORT*, *COUNTRY*, *MOVIES*, *HISTORICAL EVENTS*, *SCIENCE*, *FOOD*.
+* `q_types`: the list of question types, where the types are *what*, *why*, *when*, *who*, *where*, and *how*.  If empty, none of the those types are recognized in this question.
+* `is_paraphrase`: *True* if this question is a paragraph of some other question in this dataset; otherwise, *False*.
+* `candidates`: the list of sentences in the related section.
+* `answers`: the list of candidate indices containing the answer context of this question.
+
+##### Data for Experiments
+
+for experiments, each column gives:
+
+* `0`: a question where all tokens are separated.
+* `1`: a candidate of the question where all tokens are separated.
+* `2`: the label where `0` implies no answer to the question is found in this candidate and `1` implies the answer is found.
+
+#### Answer Triggering
+##### Data for Analysis
+
+for analysis, the columns are: 
+
+* `question`: the question.
+* `article`: the Wikipedia article related to this question.
+* `section`: the section in the Wikipedia article related to this question.
+* `topic`: the topic of this question, where the topics are *MUSIC*, *TV*, *TRAVEL*, *ART*, *SPORT*, *COUNTRY*, *MOVIES*, *HISTORICAL EVENTS*, *SCIENCE*, *FOOD*.
+* `q_types`: the list of question types, where the types are *what*, *why*, *when*, *who*, *where*, and *how*.  If empty, none of the those types are recognized in this question.
+* `is_paraphrase`: *True* if this question is a paragraph of some other question in this dataset; otherwise, *False*.
+* `candidate_list`: the list of 5 candidate sections:
+  * `article`: the title of the candidate article.
+  * `section`: the section in the candidate article.
+  * `candidates`: the list of sentences in this candidate section.
+  * `answers`: the list of candidate indices containing the answer context of this question (can be empty).
+
+##### Data for Experiments
+
+for experiments, each column gives:
+
+* `0`: a question where all tokens are separated.
+* `1`: a candidate of the question where all tokens are separated.
+* `2`: the label where `0` implies no answer to the question is found in this candidate and `1` implies the answer is found. 
+
+### Data Splits
+
+|      |Train| Valid| Test|
+| ---  | --- | ---  | --- |
+| Answer Selection |  5529 | 785 | 1590 |
+| Answer Triggering |  27645 | 3925 | 7950 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+To encourage research and provide an initial benchmark for selection based question answering and answer triggering tasks
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+Crowdsourced
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The purpose of this dataset is to help develop better selection-based question answering systems.
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+Apache License 2.0
+
+### Citation Information
+@InProceedings{7814688,
+  author={T. {Jurczyk} and M. {Zhai} and J. D. {Choi}},
+  booktitle={2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)}, 
+  title={SelQA: A New Benchmark for Selection-Based Question Answering}, 
+  year={2016},
+  volume={},
+  number={},
+  pages={820-827},
+  doi={10.1109/ICTAI.2016.0128}
+}
+
+### Contributions
+
+Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset.