Upload batch 94 (20 files, last=huggingface_dataset/Dataset_Card/yoruba_bbc_topics.md)

huggingface_dataset/Dataset_Card/Datatang_American_English_Colloquial_Video_Speech_Data.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for Datatang/American_English_Colloquial_Video_Speech_Data
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://bit.ly/3Oy6ymg
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+1,000 Hours - American English Colloquial Video Speech Data, collected from real website, covering multiple fields. Various attributes such as text content and speaker identity are annotated. This data set can be used for voiceprint recognition model training, construction of corpus for machine translation and algorithm research.
+                           
+For more details, please refer to the link: https://bit.ly/3Oy6ymg
+
+### Supported Tasks and Leaderboards
+
+automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
+
+### Languages
+
+American English
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing  
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions

huggingface_dataset/Dataset_Card/Fhrozen_CABankSakuraCHJP.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- ja
+license:
+- cc
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- found
+task_categories:
+- audio-classification
+- automatic-speech-recognition
+task_ids:
+- speaker-identification
+pretty_name: banksakura
+tags:
+- speech-recognition
+---
+
+# CABank Japanese CallHome Corpus
+
+- Participants:   120
+- Type of Study:  phone call
+- Location:   United States
+- Media type: audio
+- DOI:    doi:10.21415/T5H59V
+
+- Web: https://ca.talkbank.org/access/CallHome/jpn.html
+
+## Citation information
+
+Some citation here.
+In accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references.
+
+## Project Description
+
+This is the Japanese portion of CallHome.
+
+Speakers were solicited by the LDC to participate in this telephone speech collection effort via the internet, publications (advertisements), and personal contacts. A total of 200 call originators were found, each of whom placed a telephone call via a toll-free robot operator maintained by the LDC. Access to the robot operator was possible via a unique Personal Identification Number (PIN) issued by the recruiting staff at the LDC when the caller enrolled in the project. The participants were made aware that their telephone call would be recorded, as were the call recipients. The call was allowed only if both parties agreed to being recorded. Each caller was allowed to talk up to 30 minutes. Upon successful completion of the call, the caller was paid $20 (in addition to making a free long-distance telephone call). Each caller was allowed to place only one telephone call.
+
+Although the goal of the call collection effort was to have unique speakers in all calls, a handful of repeat speakers are included in the corpus. In all, 200 calls were transcribed. Of these, 80 have been designated as training calls, 20 as development test calls, and 100 as evaluation test calls. For each of the training and development test calls, a contiguous 10-minute region was selected for transcription; for the evaluation test calls, a 5-minute region was transcribed. For the present publication, only 20 of the evaluation test calls are being released; the remaining 80 test calls are being held in reserve for future LVCSR benchmark tests.
+
+After a successful call was completed, a human audit of each telephone call was conducted to verify that the proper language was spoken, to check the quality of the recording, and to select and describe the region to be transcribed. The description of the transcribed region provides information about channel quality, number of speakers, their gender, and other attributes.
+
+## Acknowledgements
+
+Andrew Yankes reformatted this corpus into accord with current versions of CHAT.

huggingface_dataset/Dataset_Card/GEM-submissions_lewtun__this-is-another-test-name__1655983383.md

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+---
+benchmark: gem
+type: prediction
+submission_name: This is another test name
+tags:
+- evaluation
+- benchmark
+---
+# GEM Submission
+
+Submission name: This is another test name
+

huggingface_dataset/Dataset_Card/GEM_indonlg.md

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+---
+annotations_creators:
+- none
+language_creators:
+- unknown
+languages:
+- unknown
+licenses:
+- mit
+multilinguality:
+- unknown
+pretty_name: indonlg
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- summarization
+task_ids:
+- unknown
+---
+
+# Dataset Card for GEM/indonlg
+
+## Dataset Description
+
+- **Homepage:** https://github.com/indobenchmark/indonlg
+- **Repository:** https://github.com/indobenchmark/indonlg
+- **Paper:** https://aclanthology.org/2021.emnlp-main.699
+- **Leaderboard:** N/A
+- **Point of Contact:** Genta Indra Winata
+
+### Link to Main Data Card
+
+You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/indonlg).
+
+### Dataset Summary 
+
+IndoNLG is a collection of various Indonesian, Javanese, and Sundanese NLG tasks including summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks.
+
+You can load the dataset via:
+```
+import datasets
+data = datasets.load_dataset('GEM/indonlg')
+```
+The data loader can be found [here](https://huggingface.co/datasets/GEM/indonlg).
+
+#### website
+[Github](https://github.com/indobenchmark/indonlg)
+
+#### paper
+[ACL Anthology](https://aclanthology.org/2021.emnlp-main.699)
+
+#### authors
+Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung
+
+## Dataset Overview
+
+### Where to find the Data and its Documentation
+
+#### Webpage
+
+<!-- info: What is the webpage for the dataset (if it exists)? -->
+<!-- scope: telescope -->
+[Github](https://github.com/indobenchmark/indonlg)
+
+#### Download
+
+<!-- info: What is the link to where the original dataset is hosted? -->
+<!-- scope: telescope -->
+[Github](https://github.com/indobenchmark/indonlg)
+
+#### Paper
+
+<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
+<!-- scope: telescope -->
+[ACL Anthology](https://aclanthology.org/2021.emnlp-main.699)
+
+#### BibTex
+
+<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
+<!-- scope: microscope -->
+```
+@inproceedings{cahyawijaya-etal-2021-indonlg, title =  '{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation ', author =  'Cahyawijaya, Samuel and Winata, Genta Indra and Wilie, Bryan and Vincentio, Karissa and Li, Xiaohong and Kuncoro, Adhiguna and Ruder, Sebastian and Lim, Zhi Yuan and Bahar, Syafri and Khodra, Masayu and Purwarianti, Ayu and Fung, Pascale ', booktitle =  'Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ', month = nov, year =  '2021 ', address =  'Online and Punta Cana, Dominican Republic ', publisher =  'Association for Computational Linguistics ', url =  'https://aclanthology.org/2021.emnlp-main.699 ', pages =  '8875--8898 ', abstract =  'Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese. ',}
+```
+
+#### Contact Name
+
+<!-- quick -->
+<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+Genta Indra Winata
+
+#### Contact Email
+
+<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+gentaindrawinata@gmail.com
+
+#### Has a Leaderboard?
+
+<!-- info: Does the dataset have an active leaderboard? -->
+<!-- scope: telescope -->
+no
+
+
+### Languages and Intended Use
+
+#### Multilingual?
+
+<!-- quick -->
+<!-- info: Is the dataset multilingual? -->
+<!-- scope: telescope -->
+yes
+
+#### Covered Languages
+
+<!-- quick -->
+<!-- info: What languages/dialects are covered in the dataset? -->
+<!-- scope: telescope -->
+`Indonesian`, `Javanese`, `Sundanese`
+
+#### License
+
+<!-- quick -->
+<!-- info: What is the license of the dataset? -->
+<!-- scope: telescope -->
+mit: MIT License
+
+#### Intended Use
+
+<!-- info: What is the intended use of the dataset? -->
+<!-- scope: microscope -->
+IndoNLG is a collection of Natural Language Generation (NLG) resources for Bahasa Indonesia with 10 downstream tasks.
+
+#### Primary Task
+
+<!-- info: What primary task does the dataset support? -->
+<!-- scope: telescope -->
+Summarization
+
+#### Communicative Goal
+
+<!-- quick -->
+<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
+<!-- scope: periscope -->
+Generate a response according to the context and text.
+
+
+### Credit
+
+#### Curation Organization Type(s)
+
+<!-- info: In what kind of organization did the dataset curation happen? -->
+<!-- scope: telescope -->
+`academic`, `industry`
+
+#### Curation Organization(s)
+
+<!-- info: Name the organization(s). -->
+<!-- scope: periscope -->
+The Hong Kong University of Science and Technology, Gojek, Institut Teknologi Bandung, Universitas Multimedia Nusantara, DeepMind, Prosa.ai
+
+#### Dataset Creators
+
+<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
+<!-- scope: microscope -->
+Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung
+
+#### Funding
+
+<!-- info: Who funded the data creation? -->
+<!-- scope: microscope -->
+The Hong Kong University of Science and Technology, Gojek, Institut Teknologi Bandung, Universitas Multimedia Nusantara, DeepMind, Prosa.ai
+
+#### Who added the Dataset to GEM?
+
+<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
+<!-- scope: microscope -->
+Genta Indra Winata (The Hong Kong University of Science and Technology)
+
+
+### Dataset Structure
+
+
+
+
+## Dataset in GEM
+
+### Rationale for Inclusion in GEM
+
+#### Similar Datasets
+
+<!-- info: Do other datasets for the high level task exist? -->
+<!-- scope: telescope -->
+yes
+
+#### Unique Language Coverage
+
+<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
+<!-- scope: periscope -->
+no
+
+
+### GEM-Specific Curation
+
+#### Modificatied for GEM?
+
+<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
+<!-- scope: telescope -->
+yes
+
+#### GEM Modifications
+
+<!-- info: What changes have been made to he original dataset? -->
+<!-- scope: periscope -->
+`other`
+
+#### Additional Splits?
+
+<!-- info: Does GEM provide additional splits to the dataset? -->
+<!-- scope: telescope -->
+no
+
+
+### Getting Started with the Task
+
+
+
+
+## Previous Results
+
+### Previous Results
+
+#### Measured Model Abilities
+
+<!-- info: What aspect of model ability can be measured with this dataset? -->
+<!-- scope: telescope -->
+Dialog understanding, summarization, translation
+
+#### Metrics
+
+<!-- info: What metrics are typically used for this task? -->
+<!-- scope: periscope -->
+`BLEU`
+
+#### Proposed Evaluation
+
+<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
+<!-- scope: microscope -->
+BLEU evaluates the generation quality.
+
+#### Previous results available?
+
+<!-- info: Are previous results available? -->
+<!-- scope: telescope -->
+yes
+
+#### Other Evaluation Approaches
+
+<!-- info: What evaluation approaches have others used? -->
+<!-- scope: periscope -->
+BLEU
+
+
+
+## Dataset Curation
+
+### Original Curation
+
+#### Sourced from Different Sources
+
+<!-- info: Is the dataset aggregated from different data sources? -->
+<!-- scope: telescope -->
+no
+
+
+### Language Data
+
+#### How was Language Data Obtained?
+
+<!-- info: How was the language data obtained? -->
+<!-- scope: telescope -->
+`Crowdsourced`
+
+#### Where was it crowdsourced?
+
+<!-- info: If crowdsourced, where from? -->
+<!-- scope: periscope -->
+`Participatory experiment`
+
+#### Data Validation
+
+<!-- info: Was the text validated by a different worker or a data curator? -->
+<!-- scope: telescope -->
+validated by data curator
+
+#### Was Data Filtered?
+
+<!-- info: Were text instances selected or filtered? -->
+<!-- scope: telescope -->
+not filtered
+
+
+### Structured Annotations
+
+#### Additional Annotations?
+
+<!-- quick -->
+<!-- info: Does the dataset have additional annotations for each instance? -->
+<!-- scope: telescope -->
+none
+
+#### Annotation Service?
+
+<!-- info: Was an annotation service used? -->
+<!-- scope: telescope -->
+no
+
+
+### Consent
+
+#### Any Consent Policy?
+
+<!-- info: Was there a consent policy involved when gathering the data? -->
+<!-- scope: telescope -->
+yes
+
+#### Consent Policy Details
+
+<!-- info: What was the consent policy? -->
+<!-- scope: microscope -->
+Annotators agree using the dataset for research purpose.
+
+#### Other Consented Downstream Use
+
+<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
+<!-- scope: microscope -->
+Any
+
+
+### Private Identifying Information (PII)
+
+#### Contains PII?
+
+<!-- quick -->
+<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
+<!-- scope: telescope -->
+unlikely
+
+#### Categories of PII
+
+<!-- info: What categories of PII are present or suspected in the data? -->
+<!-- scope: periscope -->
+``
+
+
+### Maintenance
+
+#### Any Maintenance Plan?
+
+<!-- info: Does the original dataset have a maintenance plan? -->
+<!-- scope: telescope -->
+no
+
+
+
+## Broader Social Context
+
+### Previous Work on the Social Impact of the Dataset
+
+#### Usage of Models based on the Data
+
+<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
+<!-- scope: telescope -->
+no
+
+
+### Impact on Under-Served Communities
+
+#### Addresses needs of underserved Communities?
+
+<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
+<!-- scope: telescope -->
+yes
+
+
+### Discussion of Biases
+
+#### Any Documented Social Biases?
+
+<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
+<!-- scope: telescope -->
+no
+
+
+
+## Considerations for Using the Data
+
+### PII Risks and Liability
+
+#### Potential PII Risk
+
+<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
+<!-- scope: microscope -->
+No
+
+
+### Licenses
+
+#### Copyright Restrictions on the Dataset
+
+<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
+<!-- scope: periscope -->
+`open license`
+
+#### Copyright Restrictions on the Language Data
+
+<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
+<!-- scope: periscope -->
+`open license`
+
+
+### Known Technical Limitations
+
+
+

huggingface_dataset/Dataset_Card/SetFit_SentEval-CR.md

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+# SentEval Customer Reviews
+
+This dataset is a port of the official [SentEval `CR` dataset](https://nlp.stanford.edu/~sidaw/home/projects:nbsvm) from [this paper](https://dl.acm.org/doi/10.1145/1014052.1014073). The test split was created from the by randomly sampling 20% of the original data and the train split is the remaining 80%. there are no official train/test splits of CR.
+There is no validation split. This was used in the STraTA paper.

huggingface_dataset/Dataset_Card/Ziyang_CC4M.md

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+The training and validation files of the conceptual captions dataset (4M).

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v3-math-468e93-2011366581.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- mathemakitten/winobias_antistereotype_test_cot_v3
+eval_info:
+  task: text_zero_shot_classification
+  model: inverse-scaling/opt-125m_eval
+  metrics: []
+  dataset_name: mathemakitten/winobias_antistereotype_test_cot_v3
+  dataset_config: mathemakitten--winobias_antistereotype_test_cot_v3
+  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: inverse-scaling/opt-125m_eval
+* Dataset: mathemakitten/winobias_antistereotype_test_cot_v3
+* Config: mathemakitten--winobias_antistereotype_test_cot_v3
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-00ac2adb-9115200.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- cifar10
+eval_info:
+  task: image_multi_class_classification
+  model: jimypbr/cifar10_outputs
+  metrics: []
+  dataset_name: cifar10
+  dataset_config: plain_text
+  dataset_split: test
+  col_mapping:
+    image: img
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Multi-class Image Classification
+* Model: jimypbr/cifar10_outputs
+* Dataset: cifar10
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@davidberg](https://huggingface.co/davidberg) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-5a4fda18-6304-4b90-86c0-99202bfbe1e9-4644.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- emotion
+eval_info:
+  task: multi_class_classification
+  model: autoevaluate/multi-class-classification
+  metrics: ['matthews_correlation']
+  dataset_name: emotion
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: text
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Multi-class Text Classification
+* Model: autoevaluate/multi-class-classification
+* Dataset: emotion
+* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/dart.md

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+---
+annotations_creators:
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+- machine-generated
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|wikitable_questions
+- extended|wikisql
+- extended|web_nlg
+- extended|cleaned_e2e
+task_categories:
+- tabular-to-text
+task_ids:
+- rdf-to-text
+paperswithcode_id: dart
+pretty_name: DART
+dataset_info:
+  features:
+  - name: tripleset
+    sequence:
+      sequence: string
+  - name: subtree_was_extended
+    dtype: bool
+  - name: annotations
+    sequence:
+    - name: source
+      dtype: string
+    - name: text
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 12966443
+    num_examples: 30526
+  - name: validation
+    num_bytes: 1458106
+    num_examples: 2768
+  - name: test
+    num_bytes: 2657644
+    num_examples: 5097
+  download_size: 29939366
+  dataset_size: 17082193
+---
+
+# Dataset Card for DART
+
+## 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:** [homepahe](https://github.com/Yale-LILY/dart)
+- **Repository:** [github](https://github.com/Yale-LILY/dart)
+- **Paper:** [paper](https://arxiv.org/abs/2007.02871)
+- **Leaderboard:** [leaderboard](https://github.com/Yale-LILY/dart#leaderboard)
+
+### Dataset Summary
+
+DART is a large dataset for open-domain structured data record to text generation. We consider the structured data record input as a set of RDF entity-relation triples, a format widely used for knowledge representation and semantics description. DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. This hierarchical, structured format with its open-domain nature differentiates DART from other existing table-to-text corpora.
+
+### Supported Tasks and Leaderboards
+
+The task associated to DART is text generation from data records that are RDF triplets:
+
+- `rdf-to-text`: The dataset can be used to train a model for text generation from RDF triplets, which consists in generating textual description of structured data. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [METEOR](https://huggingface.co/metrics/meteor), [BLEURT](https://huggingface.co/metrics/bleurt), [TER](https://huggingface.co/metrics/ter), [MoverScore](https://huggingface.co/metrics/mover_score), and [BERTScore](https://huggingface.co/metrics/bert_score). The ([BART-large model](https://huggingface.co/facebook/bart-large) from [BART](https://huggingface.co/transformers/model_doc/bart.html)) model currently achieves the following scores:
+
+|            | BLEU  | METEOR | TER  | MoverScore  | BERTScore  | BLEURT |
+| -----      | ----- | ------ | ---- | ----------- | ---------- | ------ |
+| BART       | 37.06 | 0.36   | 0.57 | 0.44        | 0.92       | 0.22   |
+
+This task has an active leaderboard which can be found [here](https://github.com/Yale-LILY/dart#leaderboard) and ranks models based on the above metrics while also reporting.
+
+### Languages
+
+The dataset is in english (en).
+
+## Dataset Structure
+
+### Data Instances
+
+Here is an example from the dataset:
+
+```
+{'annotations': {'source': ['WikiTableQuestions_mturk'],
+  'text': ['First Clearing\tbased on Callicoon, New York and location at On NYS 52 1 Mi. Youngsville']},
+ 'subtree_was_extended': False,
+ 'tripleset': [['First Clearing', 'LOCATION', 'On NYS 52 1 Mi. Youngsville'],
+  ['On NYS 52 1 Mi. Youngsville', 'CITY_OR_TOWN', 'Callicoon, New York']]}
+```
+
+It contains one annotation where the textual description is 'First Clearing\tbased on Callicoon, New York and location at On NYS 52 1 Mi. Youngsville'. The RDF triplets considered to generate this description are in tripleset and are formatted as subject, predicate, object.
+
+### Data Fields
+
+The different fields are:
+
+- `annotations`:
+  - `text`: list of text descriptions of the triplets
+  - `source`: list of sources of the RDF triplets (WikiTable, e2e, etc.)
+- `subtree_was_extended`: boolean, if the subtree condidered during the dataset construction was extended. Sometimes this field is missing, and therefore set to `None`
+- `tripleset`: RDF triplets as a list of triplets of strings (subject, predicate, object)
+
+### Data Splits
+
+There are three splits, train, validation and test:
+
+|                  | train | validation | test |
+| -----            |------:|-----------:|-----:|
+| N. Examples      | 30526 |       2768 | 6959 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+Automatically generating textual descriptions from structured data inputs is crucial to improving the accessibility of knowledge bases to lay users.
+
+### Source Data
+
+DART comes from existing datasets that cover a variety of different domains while allowing to build a tree ontology and form RDF triple sets as semantic representations. The datasets used are WikiTableQuestions, WikiSQL, WebNLG and Cleaned E2E.
+
+#### Initial Data Collection and Normalization
+
+DART is constructed using multiple complementary methods: (1) human annotation on open-domain Wikipedia tables
+from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017), (2) automatic conversion of questions in WikiSQL to declarative sentences, and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019)
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+DART is constructed using multiple complementary methods: (1) human annotation on open-domain Wikipedia tables
+from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017), (2) automatic conversion of questions in WikiSQL to declarative sentences, and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019)
+
+#### Annotation process
+
+The two stage annotation process for constructing tripleset sentence pairs is based on a tree-structured ontology of each table.
+First, internal skilled annotators denote the parent column for each column header.
+Then, a larger number of annotators provide a sentential description of an automatically-chosen subset of table cells in a row.
+
+#### 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
+
+Under MIT license (see [here](https://github.com/Yale-LILY/dart/blob/master/LICENSE))
+
+### Citation Information
+
+```
+@article{radev2020dart,
+  title={DART: Open-Domain Structured Data Record to Text Generation},
+  author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwanto and Jessica Pan and Faiaz Rahman and Ahmad Zaidi and Murori Mutuma and Yasin Tarabar and Ankit Gupta and Tao Yu and Yi Chern Tan and Xi Victoria Lin and Caiming Xiong and Richard Socher},
+  journal={arXiv preprint arXiv:2007.02871},
+  year={2020}
+```
+
+### Contributions
+
+Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset.

huggingface_dataset/Dataset_Card/hearmeneigh_e621-rising-v1-raw.md

@@ -0,0 +1,84 @@
+---
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1192534908282.634
+    num_examples: 2905671
+  download_size: 210413447679
+  dataset_size: 1192534908282.634
+pretty_name: 'E621 Rising: Raw Image Dataset v1'
+size_categories:
+- 1M<n<10M
+viewer: false
+---
+
+> # Deprecation Notice!
+> [This dataset has been superseded by v2](https://huggingface.co/datasets/hearmeneigh/e621-rising-v2-raw). Use v2 instead of this dataset.
+
+
+**Warning: THIS dataset is NOT suitable for use by minors. The dataset contains X-rated/NFSW content.**
+
+# E621 Rising: Raw Image Dataset v1
+
+**2,905,671** images (~1.1TB) downloaded from `e621.net` with [tags](https://huggingface.co/datasets/hearmeneigh/e621-rising-v1-raw/raw/main/meta/tag-counts.json).
+
+This is a raw, uncurated, and largely unprocessed dataset. You likely want to use the curated version, [available here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v1-curated). This dataset contains all kinds of NFSW material. You have been warned.
+
+## Image Processing
+* Only `jpg` and `png` images were considered
+* Image width and height have been clamped to `(0, 4096]px`; larger images have been resized to meet the limit
+* Alpha channels have been removed
+* All images have been converted to `jpg` format
+* All images have been converted to TrueColor `RGB`
+* All images have been verified to load with `Pillow`
+* Metadata from E621 is [available here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v1-raw/tree/main/meta).
+
+## Tags
+For a comprehensive list of tags and counts, [see here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v1-raw/raw/main/meta/tag-counts.json).
+
+### Changes From E621
+* Symbols have been prefixed with `symbol:`, e.g. `symbol:<3`
+* Aspect ratio has been prefixed with `aspect_ratio:`, e.g. `aspect_ratio:16_9`
+* All categories except `general` have been prefixed with the category name, e.g. `artist:somename`. The categories are:
+  * `artist`
+  * `copyright`
+  * `character`
+  * `species`
+  * `invalid`
+  * `meta`
+  * `lore`
+
+### Additional Tags
+* Image rating
+  * `rating:explicit`
+  * `rating:questionable`
+  * `rating:safe`
+* Image score
+  * `score:above_250`
+  * `score:above_500`
+  * `score:above_1000`
+  * `score:above_1500`
+  * `score:above_2000`
+  * `score:below_250`
+  * `score:below_100`
+  * `score:below_50`
+  * `score:below_25`
+  * `score:below_0`
+* Image favorites
+  * `favorites:above_4000`
+  * `favorites:above_3000`
+  * `favorites:above_2000`
+  * `favorites:above_1000`
+  * `favorites:below_1000`
+  * `favorites:below_500`
+  * `favorites:below_250`
+  * `favorites:below_100`
+  * `favorites:below_50`
+  * `favorites:below_25`

huggingface_dataset/Dataset_Card/hoskinson-center_proofnet.md

@@ -0,0 +1,51 @@
+---
+license: mit
+---
+
+# ProofNet
+
+## Dataset Description
+
+- **Repository:** [zhangir-azerbayev/ProofNet](https://github.com/zhangir-azerbayev/ProofNet)
+- **Paper:** [ProofNet](https://mathai2022.github.io/papers/20.pdf)
+- **Point of Contact:** [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/)
+
+### Dataset Summary
+ProofNet is a benchmark for autoformalization and formal proving of undergraduate-level mathematics. The ProofNet benchmarks consists of 371 examples, each consisting of a formal theorem statement in Lean 3, a natural language theorem statement, and a natural language proof. The problems are primarily drawn from popular undergraduate pure mathematics textbooks and cover topics such as real and complex analysis, linear algebra, abstract algebra, and topology. We intend for ProofNet to be a challenging benchmark that will drive progress in autoformalization and automatic theorem proving.
+
+**Citation**:
+```bibtex
+@misc{azerbayev2023proofnet,
+      title={ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics}, 
+      author={Zhangir Azerbayev and Bartosz Piotrowski and Hailey Schoelkopf and Edward W. Ayers and Dragomir Radev and Jeremy Avigad},
+      year={2023},
+      eprint={2302.12433},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+### Leaderboard
+**Statement Autoformalization**
+| Model                              | Typecheck Rate | Accuracy |
+| ---------------------------------- | -------------- | -------- |
+| Davinci-code-002 (prompt retrieval)| 45.2           | 16.1     |
+| Davinci-code-002 (in-context learning) | 23.7       | 13.4     |
+| proofGPT-1.3B                      | 10.7           | 3.2      |
+
+**Statement Informalization**
+| Model                              | Accuracy |
+| ---------------------------------- | -------- |
+| Code-davinci-002 (in-context learning)| 62.3    |
+| proofGPT-6.7B (in-context learning) | 6.5      |
+| proofGPT-1.3B (in-context learning) | 4.3      |
+
+### Data Fields
+
+- `id`: Unique string identifier for the problem.
+- `nl_statement`: Natural language theorem statement.
+- `nl_proof`: Natural language proof, in LaTeX. Depends on `amsthm, amsmath, amssymb` packages.
+- `formal_statement`: Formal theorem statement in Lean 3.
+- `src_header`: File header including imports, namespaces, and locales required for the formal statement. Note that local import of [common.lean](https://github.com/zhangir-azerbayev/ProofNet/blob/main/benchmark/benchmark_to_publish/formal/common.lean), which has to be manually downloaded and place in the same directory as your `.lean` file containing the formal statement. 
+
+### Authors
+Zhangir Azerbayev, Bartosz Piotrowski, Jeremy Avigad

huggingface_dataset/Dataset_Card/huggingartists_sugar-ray.md

@@ -0,0 +1,198 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/sugar-ray"
+
+## 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:** 0.164888 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/8b5c8fe74f6176047b2b5681e0e0e2d4.273x273x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/sugar-ray">
+      <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">Sugar Ray</div>
+    <a href="https://genius.com/artists/sugar-ray">
+    	<div style="text-align: center; font-size: 14px;">@sugar-ray</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/sugar-ray).
+
+### 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/sugar-ray")
+```
+
+## 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|
+|------:|---------:|---:|
+|117|         -|   -|
+
+'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/sugar-ray")
+
+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/irds_codec_politics.md

@@ -0,0 +1,49 @@
+---
+pretty_name: '`codec/politics`'
+viewer: false
+source_datasets: ['irds/codec']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `codec/politics`
+
+The `codec/politics` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/codec#codec/politics).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=14
+ - `qrels`: (relevance assessments); count=2,192
+
+ - For `docs`, use [`irds/codec`](https://huggingface.co/datasets/irds/codec)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/codec_politics', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
+
+qrels = load_dataset('irds/codec_politics', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ...}
+
+```
+
+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
+
+```
+@inproceedings{mackie2022codec,
+   title={CODEC: Complex Document and Entity Collection},
+   author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
+   booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+   year={2022}
+}
+```

huggingface_dataset/Dataset_Card/irds_mr-tydi_ar_train.md

@@ -0,0 +1,55 @@
+---
+pretty_name: '`mr-tydi/ar/train`'
+viewer: false
+source_datasets: ['irds/mr-tydi_ar']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mr-tydi/ar/train`
+
+The `mr-tydi/ar/train` 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/ar/train).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=12,377
+ - `qrels`: (relevance assessments); count=12,377
+
+ - For `docs`, use [`irds/mr-tydi_ar`](https://huggingface.co/datasets/irds/mr-tydi_ar)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/mr-tydi_ar_train', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mr-tydi_ar_train', '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/nateraw_world-happiness.md

@@ -0,0 +1,162 @@
+---
+license:
+- cc0-1.0
+converted_from: kaggle
+kaggle_id: unsdsn/world-happiness
+---
+
+# Dataset Card for World Happiness Report
+
+## 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/unsdsn/world-happiness
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+### Context 
+
+The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness. 
+
+### Content
+
+The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.
+
+
+### Inspiration
+
+What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness? How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports? Did any country experience a significant increase or decrease in happiness?
+
+**What is Dystopia?**
+
+Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia.
+
+**What are the residuals?**
+
+The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2014-2016 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2014- 2016 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.2, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.85 on the 0 to 10 scale.
+
+**What do the columns succeeding the Happiness Score(like Family, Generosity, etc.) describe?**
+
+The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. 
+The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) +  the Residual value or the unexplained value for each country as stated in the previous answer.
+
+If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores.
+
+#[Start a new kernel][1]
+
+
+  [1]: https://www.kaggle.com/unsdsn/world-happiness/kernels?modal=true
+
+### 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 [@unsdsn](https://kaggle.com/unsdsn)
+
+### 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/neuclir_hc4.md

@@ -0,0 +1,89 @@
+---
+annotations_creators:
+- no-annotation
+language:
+- fa
+- ru
+- zh
+language_creators:
+- found
+license:
+- odc-by
+multilinguality:
+- multilingual
+pretty_name: HC4
+size_categories:
+- 1M<n<10M
+source_datasets:
+- extended|c4
+tags: []
+task_categories:
+- text-retrieval
+task_ids:
+- document-retrieval
+---
+
+# Dataset Card for HC4
+
+## Dataset Description
+
+- **Repository:** https://github.com/hltcoe/HC4
+- **Paper:** https://arxiv.org/abs/2201.09992
+
+### Dataset Summary
+
+HC4 is a suite of test collections for ad hoc Cross-Language Information Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and Russian. The documents
+are Web pages from Common Crawl in Chinese, Persian, and Russian.
+
+### Languages
+
+ - Chinese
+ - Persian
+ - Russian
+
+## Dataset Structure
+
+### Data Instances
+
+| Split           | Documents |
+|-----------------|----------:|
+| `fas` (Persian) |      486K |
+| `rus` (Russian) |      4.7M |
+| `zho` (Chinese) |      646K |
+
+### Data Fields
+ - `id`: unique identifier for this document
+ - `cc_file`: source file from connon crawl
+ - `time`: extracted date/time from article
+ - `title`: title extracted from article
+ - `text`: extracted article body
+ - `url`: source URL
+
+## Dataset Usage
+
+Using 🤗 Datasets:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset('neuclir/hc4')
+dataset['fas'] # Persian documents
+dataset['rus'] # Russian documents
+dataset['zho'] # Chinese documents
+```
+
+## Citation Information
+
+```
+@article{Lawrie2022HC4,
+  author = {Dawn Lawrie and James Mayfield and Douglas W. Oard and Eugene Yang},
+  title = {HC4: A New Suite of Test Collections for Ad Hoc CLIR},
+  booktitle = {{Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022)},
+  year = {2022},
+  month = apr,
+  publisher = {Springer},
+  series = {Lecture Notes in Computer Science},
+  site = {Stavanger, Norway},
+  url = {https://arxiv.org/abs/2201.09992}
+}
+```

huggingface_dataset/Dataset_Card/skt_kobest_v1.md

@@ -0,0 +1,246 @@
+---
+pretty_name: KoBEST
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- ko
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+---
+
+# Dataset Card for KoBEST
+
+## 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
+
+- **Repository:** https://github.com/SKT-LSL/KoBEST_datarepo
+- **Paper:**
+- **Point of Contact:** https://github.com/SKT-LSL/KoBEST_datarepo/issues
+
+### Dataset Summary
+
+KoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean.
+
+### Supported Tasks and Leaderboards
+
+Boolean Question Answering, Choice of Plausible Alternatives, Words-in-Context, HellaSwag, Sentiment Negation Recognition
+
+### Languages
+
+`ko-KR`
+
+## Dataset Structure
+
+### Data Instances
+
+#### KB-BoolQ
+An example of a data point looks as follows.
+```
+{'paragraph': '두아 리파(Dua Lipa, 1995년 8월 22일 ~ )는 잉글랜드의 싱어송라이터, 모델이다. BBC 사운드 오브 2016 명단에 노미닛되었다. 싱글 "Be the One"가 영국 싱글 차트 9위까지 오르는 등 성과를 보여주었다.',
+ 'question': '두아 리파는 영국인인가?',
+ 'label': 1}
+```
+
+#### KB-COPA
+An example of a data point looks as follows.
+```
+{'premise': '물을 오래 끓였다.',
+ 'question': '결과',
+ 'alternative_1': '물의 양이 늘어났다.',
+ 'alternative_2': '물의 양이 줄어들었다.',
+ 'label': 1}
+```
+
+#### KB-WiC
+An example of a data point looks as follows.
+```
+{'word': '양분',
+ 'context_1': '토양에 [양분]이 풍부하여 나무가 잘 자란다.	',
+ 'context_2': '태아는 모체로부터 [양분]과 산소를 공급받게 된다.',
+ 'label': 1}
+```
+
+#### KB-HellaSwag
+An example of a data point looks as follows.
+```
+{'context': '모자를 쓴 투수가 타자에게 온 힘을 다해 공을 던진다. 공이 타자에게 빠른 속도로 다가온다. 타자가 공을 배트로 친다. 배트에서 깡 소리가 난다. 공이 하늘 위로 날아간다.',
+ 'ending_1': '외야수가 떨어지는 공을 글러브로 잡는다.',
+ 'ending_2': '외야수가 공이 떨어질 위치에 자리를 잡는다.',
+ 'ending_3': '심판이 아웃을 외친다.',
+ 'ending_4': '외야수가 공을 따라 뛰기 시작한다.',
+ 'label': 3}
+```
+
+#### KB-SentiNeg
+An example of a data point looks as follows.
+```
+{'sentence': '택배사 정말 마음에 듬',
+ 'label': 1}
+```
+
+### Data Fields
+
+### KB-BoolQ
++ `paragraph`: a `string` feature
++ `question`: a `string` feature
++ `label`: a classification label, with possible values `False`(0) and `True`(1)
+
+
+### KB-COPA
++ `premise`: a `string` feature
++ `question`: a `string` feature
++ `alternative_1`: a `string` feature
++ `alternative_2`: a `string` feature
++ `label`: an answer candidate label, with possible values `alternative_1`(0) and `alternative_2`(1)
+
+
+### KB-WiC
++ `target_word`: a `string` feature
++ `context_1`: a `string` feature
++ `context_2`: a `string` feature
++ `label`: a classification label, with possible values `False`(0) and `True`(1)
+
+### KB-HellaSwag
++ `target_word`: a `string` feature
++ `context_1`: a `string` feature
++ `context_2`: a `string` feature
++ `label`: a classification label, with possible values `False`(0) and `True`(1)
+
+### KB-SentiNeg
++ `sentence`: a `string` feature
++ `label`: a classification label, with possible values `Negative`(0) and `Positive`(1)
+
+
+### Data Splits
+
+#### KB-BoolQ
+
++ train: 3,665
++ dev: 700
++ test: 1,404
+
+#### KB-COPA
+
++ train: 3,076
++ dev: 1,000
++ test: 1,000
+
+#### KB-WiC
+
++ train: 3,318
++ dev: 1,260
++ test: 1,260
+
+#### KB-HellaSwag
+
++ train: 3,665
++ dev: 700
++ test: 1,404
+
+#### KB-SentiNeg
+
++ train: 3,649
++ dev: 400
++ test: 397
++ test_originated: 397 (Corresponding training data where the test set is originated from.)
+
+## 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
+
+```
+@misc{https://doi.org/10.48550/arxiv.2204.04541,
+  doi = {10.48550/ARXIV.2204.04541},
+  url = {https://arxiv.org/abs/2204.04541},
+  author = {Kim, Dohyeong and Jang, Myeongjun and Kwon, Deuk Sin and Davis, Eric},
+  title = {KOBEST: Korean Balanced Evaluation of Significant Tasks},
+  publisher = {arXiv},
+  year = {2022},
+}
+```
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@MJ-Jang](https://github.com/MJ-Jang) for adding this dataset.

huggingface_dataset/Dataset_Card/yoruba_bbc_topics.md

@@ -0,0 +1,179 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- yo
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- topic-classification
+pretty_name: Yoruba Bbc News Topic Classification Dataset (YorubaBbcTopics)
+dataset_info:
+  features:
+  - name: news_title
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': africa
+          '1': entertainment
+          '2': health
+          '3': nigeria
+          '4': politics
+          '5': sport
+          '6': world
+  - name: date
+    dtype: string
+  - name: bbc_url_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 197117
+    num_examples: 1340
+  - name: validation
+    num_bytes: 27771
+    num_examples: 189
+  - name: test
+    num_bytes: 55652
+    num_examples: 379
+  download_size: 265480
+  dataset_size: 280540
+---
+
+# Dataset Card for Yoruba BBC News Topic Classification dataset (yoruba_bbc_topics)
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** -
+- **Repository:** https://github.com/uds-lsv/transfer-distant-transformer-african
+- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
+- **Leaderboard:** -
+- **Point of Contact:** Michael A. Hedderich and David Adelani 
+{mhedderich, didelani} (at) lsv.uni-saarland.de
+
+### Dataset Summary
+
+A news headline topic classification dataset, similar to AG-news, for Yorùbá. The news headlines were collected from [BBC Yoruba](https://www.bbc.com/yoruba).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Yorùbá (ISO 639-1: yo)
+
+## Dataset Structure
+
+### Data Instances
+
+An instance consists of a news title sentence and the corresponding topic label as well as publishing information (date and website id).
+
+### Data Fields
+
+- `news_title`: A news title.
+- `label`: The label describing the topic of the news title. Can be one of the following classes: africa, entertainment, health, nigeria, politics, sport or world.
+- `date`: The publication date (in Yorùbá).
+- `bbc_url_id`: The identifier of the article in the BBC URL.
+
+### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset.

huggingface_dataset/Dataset_Card/zpn_delaney.md

@@ -0,0 +1,110 @@
+---
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: delaney
+size_categories:
+- n<1K
+source_datasets: []
+tags:
+- bio
+- bio-chem
+- molnet
+- molecule-net
+- biophysics
+task_categories:
+- other
+task_ids: []
+---
+
+# Dataset Card for delaney
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage: https://moleculenet.org/**
+- **Repository: https://github.com/deepchem/deepchem/tree/master**
+- **Paper: https://arxiv.org/abs/1703.00564**
+
+### Dataset Summary
+
+`delaney` (aka. `ESOL`) is a dataset included in [MoleculeNet](https://moleculenet.org/). Water solubility data(log solubility in mols per litre) for common organic small molecules.
+
+## Dataset Structure
+
+### Data Fields
+
+Each split contains
+
+* `smiles`: the [SMILES](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system) representation of a molecule
+* `selfies`: the [SELFIES](https://github.com/aspuru-guzik-group/selfies) representation of a molecule
+* `target`: log solubility in mols per litre
+
+### Data Splits
+
+The dataset is split into an 80/10/10 train/valid/test split using scaffold split. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was originially generated by the Pande Group at Standford
+
+### Licensing Information
+
+This dataset was originally released under an MIT license
+
+### Citation Information
+
+```
+@misc{https://doi.org/10.48550/arxiv.1703.00564,
+  doi = {10.48550/ARXIV.1703.00564},
+  
+  url = {https://arxiv.org/abs/1703.00564},
+  
+  author = {Wu, Zhenqin and Ramsundar, Bharath and Feinberg, Evan N. and Gomes, Joseph and Geniesse, Caleb and Pappu, Aneesh S. and Leswing, Karl and Pande, Vijay},
+  
+  keywords = {Machine Learning (cs.LG), Chemical Physics (physics.chem-ph), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
+  
+  title = {MoleculeNet: A Benchmark for Molecular Machine Learning},
+  
+  publisher = {arXiv},
+  
+  year = {2017},
+  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```
+
+### Contributions
+
+Thanks to [@zanussbaum](https://github.com/zanussbaum) for adding this dataset.