Upload batch 371 (20 files, last=huggingface_dataset/Dataset_Card/ilist.md)

huggingface_dataset/Dataset_Card/Gaborandi_breast_cancer_pubmed_abstracts.md

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+- This Dataset has been downloaded from PubMed
+- It has abstracts and titles that are related to Breast Cancer
+- the data has been cleaned before uploading
+- it could be used for any NLP task, such as Domain Adaptation

huggingface_dataset/Dataset_Card/MLCommons_ml_spoken_words.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- other
+language:
+- ar
+- as
+- br
+- ca
+- cnh
+- cs
+- cv
+- cy
+- de
+- dv
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fr
+- fy
+- ga
+- gn
+- ha
+- ia
+- id
+- it
+- ka
+- ky
+- lt
+- lv
+- mn
+- mt
+- nl
+- or
+- pl
+- pt
+- rm
+- ro
+- ru
+- rw
+- sah
+- sk
+- sl
+- sv
+- ta
+- tr
+- tt
+- uk
+- vi
+- zh
+license:
+- cc-by-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 10M<n<100M
+source_datasets:
+- extended|common_voice
+task_categories:
+- audio-classification
+task_ids: []
+pretty_name: Multilingual Spoken Words
+language_bcp47:
+- fy-NL
+- ga-IE
+- rm-sursilv
+- rm-vallader
+- sv-SE
+- zh-CN
+tags:
+- other-keyword-spotting
+---
+
+# Dataset Card for Multilingual Spoken Words
+
+## 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://mlcommons.org/en/multilingual-spoken-words/
+- **Repository:** https://github.com/harvard-edge/multilingual_kws
+- **Paper:** https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/fe131d7f5a6b38b23cc967316c13dae2-Paper-round2.pdf
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken
+words in 50 languages collectively spoken by over 5 billion people, for academic
+research and commercial applications in keyword spotting and spoken term search,
+licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords,
+totaling 23.4 million 1-second spoken examples (over 6,000 hours). The dataset
+has many use cases, ranging from voice-enabled consumer devices to call center
+automation. This dataset is generated by applying forced alignment on crowd-sourced sentence-level 
+audio to produce per-word timing estimates for extraction.
+All alignments are included in the dataset.
+
+Data is provided in two formats: `wav` (16KHz) and `opus` (48KHz). Default configurations look like 
+`"{lang}_{format}"`, so to load, for example, Tatar in wav format do:
+
+```python
+ds = load_dataset("MLCommons/ml_spoken_words", "tt_wav")
+```
+
+To download multiple languages in a single dataset pass list of languages to `languages` argument:
+```python
+ds = load_dataset("MLCommons/ml_spoken_words", languages=["ar", "tt", "br"])
+```
+
+To download a specific format pass it to the `format` argument (default format is `wav`):
+```python
+ds = load_dataset("MLCommons/ml_spoken_words", languages=["ar", "tt", "br"], format="opus")
+```
+Note that each time you provide different sets of languages, 
+examples are generated from scratch even if you already provided one or several of them before 
+because custom configurations are created each time (the data is **not** redownloaded though).
+
+### Supported Tasks and Leaderboards
+
+Keyword spotting, Spoken term search
+
+### Languages
+
+The dataset is multilingual. To specify several languages to download pass a list of them to the
+`languages` argument:
+
+```python
+ds = load_dataset("MLCommons/ml_spoken_words", languages=["ar", "tt", "br"])
+```
+
+The dataset contains data for the following languages:
+
+Low-resourced (<10 hours):
+* Arabic (0.1G, 7.6h)
+* Assamese (0.9M, 0.1h)
+* Breton (69M, 5.6h)
+* Chuvash (28M, 2.1h)
+* Chinese (zh-CN) (42M, 3.1h)
+* Dhivehi (0.7M, 0.04h)
+* Frisian (0.1G, 9.6h)
+* Georgian (20M, 1.4h)
+* Guarani (0.7M, 1.3h)
+* Greek (84M, 6.7h)
+* Hakha Chin (26M, 0.1h)
+* Hausa (90M, 1.0h)
+* Interlingua (58M, 4.0h)
+* Irish (38M, 3.2h)
+* Latvian (51M, 4.2h)
+* Lithuanian (21M, 0.46h)
+* Maltese (88M, 7.3h)
+* Oriya (0.7M, 0.1h)
+* Romanian (59M, 4.5h)
+* Sakha (42M, 3.3h)
+* Slovenian (43M, 3.0h)
+* Slovak (31M, 1.9h)
+* Sursilvan (61M, 4.8h)
+* Tamil (8.8M, 0.6h)
+* Vallader (14M, 1.2h)
+* Vietnamese (1.2M, 0.1h)
+
+Medium-resourced (>10 & <100 hours):
+* Czech (0.3G, 24h)
+* Dutch (0.8G, 70h)
+* Estonian (0.2G, 19h)
+* Esperanto (1.3G, 77h)
+* Indonesian (0.1G, 11h)
+* Kyrgyz (0.1G, 12h)
+* Mongolian (0.1G, 12h)
+* Portuguese (0.7G, 58h)
+* Swedish (0.1G, 12h)
+* Tatar (4G, 30h)
+* Turkish (1.3G, 29h)
+* Ukrainian (0.2G, 18h)
+
+Hig-resourced (>100 hours):
+* Basque (1.7G, 118h)
+* Catalan (8.7G, 615h)
+* English (26G, 1957h)
+* French (9.3G, 754h)
+* German (14G, 1083h)
+* Italian (2.2G, 155h)
+* Kinyarwanda (6.1G, 422h)
+* Persian (4.5G, 327h)
+* Polish (1.8G, 130h)
+* Russian (2.1G, 137h)
+* Spanish (4.9G, 349h)
+* Welsh (4.5G, 108h)
+
+## Dataset Structure
+
+### Data Instances
+
+```python
+{'file': 'абзар_common_voice_tt_17737010.opus',
+ 'is_valid': True,
+ 'language': 0,
+ 'speaker_id': '687025afd5ce033048472754c8d2cb1cf8a617e469866bbdb3746e2bb2194202094a715906f91feb1c546893a5d835347f4869e7def2e360ace6616fb4340e38',
+ 'gender': 0,
+ 'keyword': 'абзар',
+ 'audio': {'path': 'абзар_common_voice_tt_17737010.opus',
+  'array': array([2.03458695e-34, 2.03458695e-34, 2.03458695e-34, ...,
+         2.03458695e-34, 2.03458695e-34, 2.03458695e-34]),
+  'sampling_rate': 48000}}
+```
+
+### Data Fields
+
+* file: strinrelative audio path inside the archive
+* is_valid: if a sample is valid
+* language: language of an instance. Makes sense only when providing multiple languages to the 
+dataset loader (for example, `load_dataset("ml_spoken_words", languages=["ar", "tt"])`)
+* speaker_id: unique id of a speaker. Can be "NA" if an instance is invalid
+* gender: speaker gender. Can be one of `["MALE", "FEMALE", "OTHER", "NAN"]`
+* keyword: word spoken in a current sample
+* audio: a dictionary containing the relative path to the audio file, 
+the decoded audio array, and the sampling rate. 
+Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically 
+decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of 
+a large number of audio files might take a significant amount of time. 
+Thus, it is important to first query the sample index before the "audio" column, 
+i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`
+
+### Data Splits
+
+The data for each language is splitted into train / validation / test parts.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data comes form Common Voice dataset.
+
+#### 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
+
+he dataset consists of people who have donated their voice online. 
+You agree to not attempt to determine the identity of speakers.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The dataset is licensed under [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) and can be used for academic
+research and commercial applications in keyword spotting and spoken term search.
+
+### Citation Information
+
+```
+@inproceedings{mazumder2021multilingual,
+  title={Multilingual Spoken Words Corpus},
+  author={Mazumder, Mark and Chitlangia, Sharad and Banbury, Colby and Kang, Yiping and Ciro, Juan Manuel and Achorn, Keith and Galvez, Daniel and Sabini, Mark and Mattson, Peter and Kanter, David and others},
+  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+  year={2021}
+}
+```
+
+### Contributions
+
+Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

huggingface_dataset/Dataset_Card/SocialGrep_the-reddit-place-dataset.md

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+---
+annotations_creators:
+- lexyr
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+paperswithcode_id: null
+---
+
+# Dataset Card for the-reddit-place-dataset
+
+## 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)
+  - [Licensing Information](#licensing-information)
+
+## Dataset Description
+
+- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/the-reddit-place-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=theredditplacedataset)
+- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=theredditplacedataset)
+
+### Dataset Summary
+
+The written history or /r/Place, in posts and comments.
+
+
+### Languages
+
+Mainly English.
+
+## Dataset Structure
+
+### Data Instances
+
+A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
+
+### Data Fields
+
+- 'type': the type of the data point. Can be 'post' or 'comment'.
+- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
+- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
+- 'subreddit.name': the human-readable name of the data point's host subreddit.
+- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
+- 'created_utc': a UTC timestamp for the data point.
+- 'permalink': a reference link to the data point on Reddit.
+- 'score': score of the data point on Reddit.
+
+- 'domain': (Post only) the domain of the data point's link.
+- 'url': (Post only) the destination of the data point's link, if any.
+- 'selftext': (Post only) the self-text of the data point, if any.
+- 'title': (Post only) the title of the post data point.
+
+- 'body': (Comment only) the body of the comment data point.
+- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
+
+## Additional Information
+
+### Licensing Information
+
+CC-BY v4.0

huggingface_dataset/Dataset_Card/USC-MOLA-Lab_MFRC.md

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+# Dataset Card for MFRC
+
+## 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:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Reddit posts annotated for moral foundations
+
+### Supported Tasks and Leaderboards
+
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+
+
+### Data Fields
+
+- text
+- subreddit
+- bucket 
+- annotator
+- annotation
+- confidence
+
+### Data Splits
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+
+
+#### Who are the source language producers?
+
+
+
+### Annotations
+
+#### Annotation process
+
+
+#### Who are the annotators?
+
+
+### Personal and Sensitive Information
+
+
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+
+
+### Discussion of Biases
+
+
+### Other Known Limitations
+
+
+## Additional Information
+
+### Dataset Curators
+
+
+
+### Licensing Information
+
+cc-by-4.0
+
+### Citation Information
+
+```bibtex
+@misc{trager2022moral,
+    title={The Moral Foundations Reddit Corpus},
+    author={Jackson Trager and Alireza S. Ziabari and Aida Mostafazadeh Davani and Preni Golazazian and Farzan Karimi-Malekabadi and Ali Omrani and Zhihe Li and Brendan Kennedy and Nils Karl Reimer and Melissa Reyes and Kelsey Cheng and Mellow Wei and Christina Merrifield and Arta Khosravi and Evans Alvarez and Morteza Dehghani},
+    year={2022},
+    eprint={2208.05545},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+### Contributions

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-conll2003-conll2003-bc26c9-1485554295.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- conll2003
+eval_info:
+  task: entity_extraction
+  model: jjglilleberg/bert-finetuned-ner
+  metrics: []
+  dataset_name: conll2003
+  dataset_config: conll2003
+  dataset_split: test
+  col_mapping:
+    tokens: tokens
+    tags: ner_tags
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: jjglilleberg/bert-finetuned-ner
+* Dataset: conll2003
+* Config: conll2003
+* 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/autoevaluate_autoeval-staging-eval-project-5480d71b-7995081.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- cifar10
+eval_info:
+  task: image_multi_class_classification
+  model: aaraki/vit-base-patch16-224-in21k-finetuned-cifar10
+  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: aaraki/vit-base-patch16-224-in21k-finetuned-cifar10
+* Dataset: cifar10
+
+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/autoevaluate_autoeval-staging-eval-project-c230b859-684d-4c33-ba1d-1f5cafa82377-327627.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad
+eval_info:
+  task: extractive_question_answering
+  model: autoevaluate/extractive-question-answering
+  metrics: []
+  dataset_name: squad
+  dataset_config: plain_text
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: autoevaluate/extractive-question-answering
+* Dataset: squad
+* Config: plain_text
+* Split: validation
+
+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/biglam_yalta_ai_tabular_dataset.md

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+---
+annotations_creators:
+- expert-generated
+language: []
+language_creators:
+- expert-generated
+license:
+- cc-by-4.0
+multilinguality: []
+pretty_name: YALTAi Tabular Dataset
+size_categories:
+- n<1K
+source_datasets: []
+tags:
+- manuscripts
+- LAM
+task_categories:
+- object-detection
+task_ids: []
+---
+
+# YALTAi Tabular Dataset
+
+## Table of Contents
+- [YALTAi Tabular Dataset](#YALTAi-Tabular-Dataset)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [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)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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://doi.org/10.5281/zenodo.6827706](https://doi.org/10.5281/zenodo.6827706) 
+- **Paper:** [https://arxiv.org/abs/2207.11230](https://arxiv.org/abs/2207.11230)
+
+### Dataset Summary
+
+This dataset contains a subset of data used in the paper [You Actually Look Twice At it (YALTAi): using an object detectionapproach instead of region segmentation within the Kraken engine](https://arxiv.org/abs/2207.11230). This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset covers pages with tabular information with the following objects "Header", "Col", "Marginal", "text". 
+
+### Supported Tasks and Leaderboards
+
+- `object-detection`: This dataset can be used to train a model for object-detection on historic document images. 
+
+
+## Dataset Structure
+
+This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines. 
+
+- The first configuration, `YOLO`, uses the data's original format. 
+- The second configuration converts the YOLO format into a format which is closer to the `COCO` annotation format. This is done to make it easier to work with the `feature_extractor`s from the `Transformers` models for object detection, which expect data to be in a COCO style format. 
+
+### Data Instances
+
+An example instance from the COCO config:
+
+```
+{'height': 2944,
+ 'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FA413CDA210>,
+ 'image_id': 0,
+ 'objects': [{'area': 435956,
+   'bbox': [0.0, 244.0, 1493.0, 292.0],
+   'category_id': 0,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 88234,
+   'bbox': [305.0, 127.0, 562.0, 157.0],
+   'category_id': 2,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 5244,
+   'bbox': [1416.0, 196.0, 92.0, 57.0],
+   'category_id': 2,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 5720,
+   'bbox': [1681.0, 182.0, 88.0, 65.0],
+   'category_id': 2,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 374085,
+   'bbox': [0.0, 540.0, 163.0, 2295.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 577599,
+   'bbox': [104.0, 537.0, 253.0, 2283.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 598670,
+   'bbox': [304.0, 533.0, 262.0, 2285.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 56,
+   'bbox': [284.0, 539.0, 8.0, 7.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 1868412,
+   'bbox': [498.0, 513.0, 812.0, 2301.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 307800,
+   'bbox': [1250.0, 512.0, 135.0, 2280.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 494109,
+   'bbox': [1330.0, 503.0, 217.0, 2277.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 52,
+   'bbox': [1734.0, 1013.0, 4.0, 13.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []},
+  {'area': 90666,
+   'bbox': [0.0, 1151.0, 54.0, 1679.0],
+   'category_id': 1,
+   'id': 0,
+   'image_id': '0',
+   'iscrowd': False,
+   'segmentation': []}],
+ 'width': 2064}
+```
+
+An example instance from the YOLO config: 
+
+``` python
+{'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FAA140F2450>,
+ 'objects': {'bbox': [[747, 390, 1493, 292],
+   [586, 206, 562, 157],
+   [1463, 225, 92, 57],
+   [1725, 215, 88, 65],
+   [80, 1688, 163, 2295],
+   [231, 1678, 253, 2283],
+   [435, 1675, 262, 2285],
+   [288, 543, 8, 7],
+   [905, 1663, 812, 2301],
+   [1318, 1653, 135, 2280],
+   [1439, 1642, 217, 2277],
+   [1737, 1019, 4, 13],
+   [26, 1991, 54, 1679]],
+  'label': [0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]}}
+```
+
+
+
+### Data Fields
+
+The fields for the YOLO config:
+
+- `image`: the image
+- `objects`: the annotations which consist of:
+    - `bbox`: a list of bounding boxes for the image
+    - `label`: a list of labels for this image
+
+The fields for the COCO config:
+
+- `height`: height of the image
+- `width`: width of the image
+- `image`: image 
+- `image_id`: id for the image
+- `objects`: annotations in COCO format, consisting of a list containing dictionaries with the following keys:
+  - `bbox`: bounding boxes for the images
+  - `category_id`: a label for the image
+  - `image_id`: id for the image
+  - `iscrowd`: COCO `iscrowd` flag
+  - `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
+
+
+ 
+### Data Splits
+
+The dataset contains a train, validation and test split with the following numbers per split:
+
+
+|          | train | validation | test |
+|----------|-------|------------|------|
+| examples | 196   | 22         | 135  |
+
+
+## Dataset Creation
+
+> [this] dataset was produced using a single source, the Lectaurep Repertoires dataset [Rostaing et al., 2021], which served as a basis for only the training and development split. The testset is composed of original data, from various documents, from the 17th century up to the early 20th with a single soldier war report. The test set is voluntarily very different and out of domain with column borders that are not drawn nor printed in certain cases, layout in some kind of masonry layout. p.8
+.
+### Curation Rationale
+
+This dataset was created to produce a simplified version of the [Lectaurep Repertoires dataset](https://github.com/HTR-United/lectaurep-repertoires), which was found to contain:
+
+> around 16 different ways to describe columns, from Col1 to Col7, the case-different col1-col7 and finally ColPair and ColOdd, which we all reduced to Col p.8
+
+
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The LECTAUREP (LECTure Automatique de REPertoires) project, which began in 2018, is a joint initiative of the Minutier central des notaires de Paris, the National Archives and the 
+ Minutier central des notaires de Paris of the National Archives, the [ALMAnaCH (Automatic Language Modeling and Analysis & Computational Humanities)](https://www.inria.fr/en/almanach) team at Inria and the EPHE (Ecole Pratique des Hautes Etudes), in partnership with the Ministry of Culture.
+
+> The lectaurep-bronod corpus brings together 100 pages from the repertoire of Maître Louis Bronod (1719-1765), notary in Paris from December 13, 1719 to July 23, 1765. The pages concerned were written during the years 1742 to 1745.
+
+#### Who are the source language producers?
+
+[More information needed]
+
+### Annotations
+
+|          | Train | Dev | Test | Total | Average area | Median area |
+|----------|-------|-----|------|-------|--------------|-------------|
+| Col      | 724   | 105 | 829  | 1658  | 9.32         | 6.33        |
+| Header   | 103   | 15  | 42   | 160   | 6.78         | 7.10        |
+| Marginal | 60    | 8   | 0    | 68    | 0.70         | 0.71        |
+| Text     | 13    | 5   | 0    | 18    | 0.01         | 0.00        |
+|          |       |     | -    |       |              |             |
+
+
+#### Annotation process
+
+[More information needed]
+
+#### Who are the annotators?
+
+[More information needed]
+
+### Personal and Sensitive Information
+
+This data does not contain information relating to living individuals.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+A growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.
+
+### Discussion of Biases
+
+Historical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed. 
+
+### Other Known Limitations
+
+[More information needed]
+
+
+## Additional Information
+
+### Dataset Curators
+
+
+### Licensing Information
+
+[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
+
+### Citation Information
+
+```
+@dataset{clerice_thibault_2022_6827706,
+  author       = {Clérice, Thibault},
+  title        = {YALTAi: Tabular Dataset},
+  month        = jul,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {1.0.0},
+  doi          = {10.5281/zenodo.6827706},
+  url          = {https://doi.org/10.5281/zenodo.6827706}
+}
+```
+
+
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6827706.svg)](https://doi.org/10.5281/zenodo.6827706)
+
+
+    
+### Contributions
+
+Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.

huggingface_dataset/Dataset_Card/cjvt_cosimlex.md

@@ -0,0 +1,115 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- en
+- hr
+- sl
+- fi
+license:
+- gpl-3.0
+multilinguality:
+- multilingual
+size_categories:
+- n<1K
+source_datasets: []
+task_categories:
+- other
+task_ids: []
+pretty_name: CoSimLex
+tags:
+- graded-word-similarity-in-context
+---
+
+# Dataset Card for CoSimLex
+
+### Dataset Summary
+
+The dataset contains human similarity ratings for pairs of words. The annotators were presented with contexts that contained both of the words in the pair and the dataset features two different contexts per pair. The words were sourced from the English, Croatian, Finnish and Slovenian versions of the original Simlex dataset.  
+Statistics:  
+- 340 English pairs (config `en`),
+- 112 Croatian pairs (config `hr`),  
+- 111 Slovenian pairs (config `sl`),
+- 24 Finnish pairs (config `fi`).
+
+### Supported Tasks and Leaderboards
+
+Graded word similarity in context.  
+
+### Languages
+
+English, Croatian, Slovenian, Finnish.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample instance from the dataset:
+```
+{
+  'word1': 'absence', 
+  'word2': 'presence', 
+  'context1': 'African slaves from Angola and Mozambique were also present, but in fewer numbers than in other Brazilian areas, because Paraná was a poor region that did not need much slave manpower. The immigration grew in the mid-19th century, mostly composed of Italian, German, Polish, Ukrainian, and Japanese peoples. While Poles and Ukrainians are present in Paraná, their <strong>presence</strong> in the rest of Brazil is almost <strong>absence</strong>.', 
+  'context2': 'The Chinese had become almost impossible to deal with because of the turmoil associated with the cultural revolution. The North Vietnamese <strong>presence</strong> in Eastern Cambodia had grown so large that it was destabilizing Cambodia politically and economically. Further, when the Cambodian left went underground in the late 1960s, Sihanouk had to make concessions to the right in the <strong>absence</strong> of any force that he could play off against them.', 
+  'sim1': 2.2699999809265137, 
+  'sim2': 1.3700000047683716, 
+  'stdev1': 2.890000104904175, 
+  'stdev2': 1.7899999618530273, 
+  'pvalue': 0.2409999966621399, 
+  'word1_context1': 'absence', 
+  'word2_context1': 'presence', 
+  'word1_context2': 'absence', 
+  'word2_context2': 'presence'
+}
+```
+
+### Data Fields
+
+- `word1`: a string representing the first word in the pair. Uninflected form.
+- `word2`: a string representing the second word in the pair. Uninflected form.
+- `context1`: a string representing the first context containing the pair of words. The target words are marked with a `<strong></strong>` labels.
+- `context2`: a string representing the second context containing the pair of words. The target words are marked with a `<strong></strong>` labels.
+- `sim1`: a float representing the mean of the similarity scores within the first context.
+- `sim2`: a float representing the mean of the similarity scores within the second context.
+- `stdev1`: a float representing the standard Deviation for the scores within the first context.
+- `stdev2`: a float representing the standard deviation for the scores within the second context.
+- `pvalue`: a float representing the p-value calculated using the Mann-Whitney U test.
+- `word1_context1`: a string representing the inflected version of the first word as it appears in the first context.
+- `word2_context1`: a string representing the inflected version of the second word as it appears in the first context.
+- `word1_context2`: a string representing the inflected version of the first word as it appears in the second context.
+- `word2_context2`: a string representing the inflected version of the second word as it appears in the second context.
+
+## Additional Information
+
+### Dataset Curators
+
+Carlos Armendariz; et al. (please see http://hdl.handle.net/11356/1308 for the full list)
+
+### Licensing Information
+
+GNU GPL v3.0.
+
+### Citation Information
+
+```
+@inproceedings{armendariz-etal-2020-semeval,
+    title = "{SemEval-2020} {T}ask 3: Graded Word Similarity in Context ({GWSC})",
+    author = "Armendariz, Carlos S.  and
+      Purver, Matthew  and
+      Pollak, Senja  and
+      Ljube{\v{s}}i{\'{c}}, Nikola  and
+      Ul{\v{c}}ar, Matej  and
+      Robnik-{\v{S}}ikonja, Marko and
+      Vuli{\'{c}}, Ivan and
+      Pilehvar, Mohammad Taher",
+    booktitle = "Proceedings of the 14th International Workshop on Semantic Evaluation",
+    year = "2020",
+    address="Online"
+}
+```
+
+### Contributions
+
+Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.

huggingface_dataset/Dataset_Card/exams.md

@@ -0,0 +1,1173 @@
+---
+pretty_name: EXAMS
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- ar
+- bg
+- de
+- es
+- fr
+- hr
+- hu
+- it
+- lt
+- mk
+- pl
+- pt
+- sq
+- sr
+- tr
+- vi
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+- multilingual
+size_categories:
+- 10K<n<100K
+- 1K<n<10K
+- n<1K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- multiple-choice-qa
+paperswithcode_id: exams
+configs:
+- alignments
+- crosslingual_bg
+- crosslingual_hr
+- crosslingual_hu
+- crosslingual_it
+- crosslingual_mk
+- crosslingual_pl
+- crosslingual_pt
+- crosslingual_sq
+- crosslingual_sr
+- crosslingual_test
+- crosslingual_tr
+- crosslingual_vi
+- crosslingual_with_para_bg
+- crosslingual_with_para_hr
+- crosslingual_with_para_hu
+- crosslingual_with_para_it
+- crosslingual_with_para_mk
+- crosslingual_with_para_pl
+- crosslingual_with_para_pt
+- crosslingual_with_para_sq
+- crosslingual_with_para_sr
+- crosslingual_with_para_test
+- crosslingual_with_para_tr
+- crosslingual_with_para_vi
+- multilingual
+- multilingual_with_para
+dataset_info:
+- config_name: alignments
+  features:
+  - name: source_id
+    dtype: string
+  - name: target_id_list
+    sequence: string
+  splits:
+  - name: full
+    num_bytes: 1265280
+    num_examples: 10834
+  download_size: 169745177
+  dataset_size: 1265280
+- config_name: multilingual
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 3385865
+    num_examples: 7961
+  - name: validation
+    num_bytes: 1143067
+    num_examples: 2672
+  - name: test
+    num_bytes: 5753625
+    num_examples: 13510
+  download_size: 169745177
+  dataset_size: 10282557
+- config_name: multilingual_with_para
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 127298595
+    num_examples: 7961
+  - name: validation
+    num_bytes: 42713069
+    num_examples: 2672
+  - name: test
+    num_bytes: 207981218
+    num_examples: 13510
+  download_size: 169745177
+  dataset_size: 377992882
+- config_name: crosslingual_test
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: test
+    num_bytes: 8412531
+    num_examples: 19736
+  download_size: 169745177
+  dataset_size: 8412531
+- config_name: crosslingual_with_para_test
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: test
+    num_bytes: 207981218
+    num_examples: 13510
+  download_size: 169745177
+  dataset_size: 207981218
+- config_name: crosslingual_bg
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 1078545
+    num_examples: 2344
+  - name: validation
+    num_bytes: 282115
+    num_examples: 593
+  download_size: 169745177
+  dataset_size: 1360660
+- config_name: crosslingual_with_para_bg
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 47068024
+    num_examples: 2344
+  - name: validation
+    num_bytes: 11916370
+    num_examples: 593
+  download_size: 169745177
+  dataset_size: 58984394
+- config_name: crosslingual_hr
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 808320
+    num_examples: 2341
+  - name: validation
+    num_bytes: 176910
+    num_examples: 538
+  download_size: 169745177
+  dataset_size: 985230
+- config_name: crosslingual_with_para_hr
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 24890820
+    num_examples: 2341
+  - name: validation
+    num_bytes: 5695382
+    num_examples: 538
+  download_size: 169745177
+  dataset_size: 30586202
+- config_name: crosslingual_hu
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 678447
+    num_examples: 1731
+  - name: validation
+    num_bytes: 202324
+    num_examples: 536
+  download_size: 169745177
+  dataset_size: 880771
+- config_name: crosslingual_with_para_hu
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 19036575
+    num_examples: 1731
+  - name: validation
+    num_bytes: 6043577
+    num_examples: 536
+  download_size: 169745177
+  dataset_size: 25080152
+- config_name: crosslingual_it
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
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+    num_examples: 1010
+  - name: validation
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+    num_examples: 246
+  download_size: 169745177
+  dataset_size: 493207
+- config_name: crosslingual_with_para_it
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
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+      - name: para
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+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
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+    - name: subject
+      dtype: string
+    - name: language
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+    num_examples: 1010
+  - name: validation
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+    num_examples: 246
+  download_size: 169745177
+  dataset_size: 20428284
+- config_name: crosslingual_mk
+  features:
+  - name: id
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+  - name: question
+    struct:
+    - name: stem
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+    - name: choices
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+  - name: info
+    struct:
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+    - name: subject
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+    - name: language
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+    num_examples: 1665
+  - name: validation
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+    num_examples: 410
+  download_size: 169745177
+  dataset_size: 1031152
+- config_name: crosslingual_with_para_mk
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
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+      sequence:
+      - name: text
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+      - name: para
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+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
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+    - name: subject
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+    - name: language
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+    num_examples: 1665
+  - name: validation
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+    num_examples: 410
+  download_size: 169745177
+  dataset_size: 48120600
+- config_name: crosslingual_pl
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
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+      - name: para
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+  - name: answerKey
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+  - name: info
+    struct:
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+    - name: language
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+    num_examples: 1577
+  - name: validation
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+    num_examples: 394
+  download_size: 169745177
+  dataset_size: 716123
+- config_name: crosslingual_with_para_pl
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
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+      - name: para
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+  - name: info
+    struct:
+    - name: grade
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+    - name: language
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+    num_examples: 1577
+  - name: validation
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+    num_examples: 394
+  download_size: 169745177
+  dataset_size: 20533693
+- config_name: crosslingual_pt
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
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+    - name: choices
+      sequence:
+      - name: text
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+      - name: para
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+  - name: answerKey
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+  - name: info
+    struct:
+    - name: grade
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+    - name: subject
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+    - name: language
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+    num_examples: 740
+  - name: validation
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+    num_examples: 184
+  download_size: 169745177
+  dataset_size: 463064
+- config_name: crosslingual_with_para_pt
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
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+    - name: choices
+      sequence:
+      - name: text
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+      - name: para
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+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
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+    - name: subject
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+    - name: language
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+    num_examples: 740
+  - name: validation
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+    num_examples: 184
+  download_size: 169745177
+  dataset_size: 15279647
+- config_name: crosslingual_sq
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
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+    - name: choices
+      sequence:
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+      - name: para
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+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
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+    - name: subject
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+    - name: language
+      dtype: string
+  splits:
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+    num_examples: 1194
+  - name: validation
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+    num_examples: 311
+  download_size: 169745177
+  dataset_size: 534681
+- config_name: crosslingual_with_para_sq
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
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+      - name: label
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+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
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+    - name: subject
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+    - name: language
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+  - name: train
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+    num_examples: 1194
+  - name: validation
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+    num_examples: 311
+  download_size: 169745177
+  dataset_size: 21792073
+- config_name: crosslingual_sr
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
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+      - name: para
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+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
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+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 650268
+    num_examples: 1323
+  - name: validation
+    num_bytes: 145928
+    num_examples: 314
+  download_size: 169745177
+  dataset_size: 796196
+- config_name: crosslingual_with_para_sr
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
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+    num_examples: 1323
+  - name: validation
+    num_bytes: 5772713
+    num_examples: 314
+  download_size: 169745177
+  dataset_size: 30349266
+- config_name: crosslingual_tr
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
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+    num_examples: 1571
+  - name: validation
+    num_bytes: 182974
+    num_examples: 393
+  download_size: 169745177
+  dataset_size: 901405
+- config_name: crosslingual_with_para_tr
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
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+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
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+    num_examples: 1571
+  - name: validation
+    num_bytes: 4763341
+    num_examples: 393
+  download_size: 169745177
+  dataset_size: 23361304
+- config_name: crosslingual_vi
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 954191
+    num_examples: 1955
+  - name: validation
+    num_bytes: 232264
+    num_examples: 488
+  download_size: 169745177
+  dataset_size: 1186455
+- config_name: crosslingual_with_para_vi
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    struct:
+    - name: stem
+      dtype: string
+    - name: choices
+      sequence:
+      - name: text
+        dtype: string
+      - name: label
+        dtype: string
+      - name: para
+        dtype: string
+  - name: answerKey
+    dtype: string
+  - name: info
+    struct:
+    - name: grade
+      dtype: int32
+    - name: subject
+      dtype: string
+    - name: language
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 40884023
+    num_examples: 1955
+  - name: validation
+    num_bytes: 10260662
+    num_examples: 488
+  download_size: 169745177
+  dataset_size: 51144685
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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/mhardalov/exams-qa
+- **Paper:** [EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering](https://arxiv.org/abs/2011.03080)
+- **Point of Contact:** [hardalov@@fmi.uni-sofia.bg](hardalov@@fmi.uni-sofia.bg)
+
+### Dataset Summary
+
+EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations. It consists of more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The languages in the dataset are:
+- ar
+- bg
+- de
+- es
+- fr
+- hr
+- hu
+- it
+- lt
+- mk
+- pl
+- pt
+- sq
+- sr
+- tr
+- vi
+
+## Dataset Structure
+
+### Data Instances
+
+An example of a data instance (with support paragraphs, in Bulgarian) is:
+```
+{'answerKey': 'C',
+ 'id': '35dd6b52-7e71-11ea-9eb1-54bef70b159e',
+ 'info': {'grade': 12, 'language': 'Bulgarian', 'subject': 'Biology'},
+ 'question': {'choices': {'label': ['A', 'B', 'C', 'D'],
+   'para': ['Това води до наследствени изменения между организмите. Мирновременните вождове са наследствени. Черният, сивият и кафявият цвят на оцветяване на тялото се определя от пигмента меланин и възниква в резултат на наследствени изменения. Тези различия, според Монтескьо, не са наследствени. Те са и важни наследствени вещи в клана. Те са били наследствени архонти и управляват демократично. Реликвите са исторически, религиозни, семейни (наследствени) и технически. Общо са направени 800 изменения. Не всички наследствени аномалии на хемоглобина са вредни, т.е. Моногенните наследствени болести, които водят до мигрена, са редки. Няма наследствени владетели. Повечето от тях са наследствени и се предават на потомството. Всичките синове са ерцхерцози на всичките наследствени земи и претенденти. През 1509 г. Фраунбергите са издигнати на наследствени имперски графове. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Описани са единични наследствени случаи, но по-често липсва фамилна обремененост. Позициите им са наследствени и се предават в рамките на клана. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Имало двама наследствени вождове. Имало двама наследствени вождове. Годишният календар, „компасът“ и биологичния часовник са наследствени и при много бозайници.',
+    'Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения. Те се дължат както на растягането на кожата, така и на въздействието на хормоналните изменения върху кожната тъкан. тези изменения се долавят по-ясно. Впоследствие, той претъ��пява изменения. Ширината остава без изменения. След тяхното издаване се налагат изменения в първоначалния Кодекс, защото не е съобразен с направените в Дигестите изменения. Еволюционният преход се характеризира със следните изменения: Наблюдават се и сезонни изменения в теглото. Приемат се изменения и допълнения към Устава. Тук се размножават и предизвикват възпалителни изменения. Общо са направени 800 изменения. Бронирането не претърпява съществени изменения. При животните се откриват изменения при злокачествената форма. Срещат се и дегенеративни изменения в семенните каналчета. ТАВКР „Баку“ се строи по изменения проект 1143.4. Трансът се съпровожда с определени изменения на мозъчната дейност. На изменения е подложен и Светия Синод. Внесени са изменения в конструкцията на веригите. На храма са правени лоши архитектурни изменения. Оттогава стиховете претърпяват изменения няколко пъти. Настъпват съществени изменения в музикалната култура. По-късно той претърпява леки изменения. Настъпват съществени изменения в музикалната култура. Претърпява сериозни изменения само носовата надстройка. Хоризонталното брониране е оставено без изменения.',
+    'Модификациите са обратими. Тези реакции са обратими. В началните стадии тези натрупвания са обратими. Всички такива ефекти са временни и обратими. Много от реакциите са обратими и идентични с тези при гликолизата. Ако в обращение има книжни пари, те са обратими в злато при поискване . Общо са направени 800 изменения. Непоследователността е представена от принципа на "симетрия", при който взаимоотношенията са разглеждани като симетрични или обратими. Откакто формулите в клетките на електронната таблица не са обратими, тази техника е с ограничена стойност. Ефектът на Пелтие-Зеебек и ефектът Томсън са обратими (ефектът на Пелтие е обратен на ефекта на Зеебек). Плазмолизата протича в три етапа, в зависимост от силата и продължителността на въздействието:\n\nПървите два етапа са обратими. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Оттогава насетне екипите не са претърпявали съществени изменения. Изменения са направени и в колесника на машината. Тези изменения са обявени през октомври 1878 година. Последните изменения са внесени през януари 2009 година. В процеса на последващото проектиране са внесени някои изменения. Сериозните изменения са в края на Втората световна война. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения.',
+    'Ерозионни процеси ��т масов характер липсват. Обновлението в редиците на партията приема масов характер. Тя обаче няма масов характер поради спецификата на формата. Движението против десятъка придобива масов характер и в Балчишка околия. Понякога екзекутирането на „обсебените от Сатана“ взимало невероятно масов характер. Укриването на дължими като наряд продукти в селата придобива масов характер. Периодичните миграции са в повечето случаи с масов характер и са свързани със сезонните изменения в природата, а непериодичните са премествания на животни, които настъпват след пожари, замърсяване на средата, висока численост и др. Имат необратим характер. Именно по време на двувековните походи на западните рицари използването на гербовете придобива масов характер. След присъединяването на Южен Кавказ към Русия, изселването на азербайджанци от Грузия придобива масов характер. Те имат нормативен характер. Те имат установителен характер. Освобождаването на работна сила обикновено има масов характер, защото обхваща големи контингенти от носителите на труд. Валежите имат подчертано континентален характер. Имат най-често издънков характер. Приливите имат предимно полуденонощен характер. Някои от тях имат мистериален характер. Тези сведения имат случаен, епизодичен характер. Те имат сезонен или годишен характер. Временните обезпечителни мерки имат временен характер. Други имат пожелателен характер (Здравко, Слава). Ловът и събирачеството имат спомагателен характер. Фактически успяват само малко да усилят бронирането на артилерийските погреби, другите изменения носят само частен характер. Някои карикатури имат само развлекателен характер, докато други имат политически нюанси. Поемите на Хезиод имат по-приложен характер.'],
+   'text': ['дължат се на фенотипни изменения',
+    'имат масов характер',
+    'са наследствени',
+    'са обратими']},
+  'stem': 'Мутационите изменения:'}}
+```
+
+### Data Fields
+
+A data instance contains the following fields:
+- `id`: A question ID, unique across the dataset
+- `question`: the question contains the following:
+  - `stem`: a stemmed representation of the question textual
+  - `choices`: a set of 3 to 5 candidate answers, which each have:
+    - `text`: the text of the answers
+    - `label`: a label in `['A', 'B', 'C', 'D', 'E']` used to match to the `answerKey`
+    - `para`: (optional) a supported paragraph from Wikipedia in the same language as the question and answer
+- `answerKey`: the key corresponding to the right answer's `label`
+- `info`: some additional information on the question including:
+  - `grade`: the school grade for the exam this question was taken from
+  - `subject`: a free text description of the academic subject
+  - `language`: the English name of the language for this question
+
+### Data Splits
+
+Depending on the configuration, the dataset have different splits:
+- "alignments": a single "full" split
+- "multilingual" and "multilingual_with_para": "train", "validation" and "test" splits
+- "crosslingual_test" and "crosslingual_with_para_test": a single "test" split
+- the rest of crosslingual configurations: "train" and "validation" splits
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Eχαµs was collected from official state exams prepared by the ministries of education of various countries. These exams are taken by students graduating from high school, and often require knowledge learned through the entire course.
+
+The questions cover a large variety of subjects and material based on the country’s education system. They cover major school subjects such as Biology, Chemistry, Geography, History, and Physics, but we also  highly specialized ones such as Agriculture, Geology, Informatics, as well as some applied and profiled studies.
+
+Some countries allow students to take official examinations in several languages. This dataset provides 9,857 parallel question pairs spread across seven languages coming from Croatia (Croatian, Serbian, Italian, Hungarian), Hungary (Hungarian, German, French, Spanish, Croatian, Serbian, Italian), and North Macedonia (Macedonian, Albanian, Turkish).
+
+For all languages in the dataset, the first step in the process of data collection was to download the PDF files per year, per subject, and per language (when parallel languages were available in the same source), convert the PDF files to text, and select those that were well formatted and followed the document structure.
+
+Then, Regular Expressions (RegEx) were used to parse the questions, their corresponding choices and the correct answer choice. In order to ensure that all our questions are answerable using textual input only, questions that contained visual information were removed, as selected by using curated list of words such as map, table, picture, graph, etc., in the corresponding language.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The dataset, which contains paragraphs from Wikipedia, is licensed under CC-BY-SA 4.0. The code in this repository is licensed according the [LICENSE file](https://raw.githubusercontent.com/mhardalov/exams-qa/main/LICENSE).
+
+### Citation Information
+
+```
+@article{hardalov2020exams,
+  title={EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering},
+  author={Hardalov, Momchil and Mihaylov, Todor and Dimitrina Zlatkova and Yoan Dinkov and Ivan Koychev and Preslav Nvakov},
+  journal={arXiv preprint arXiv:2011.03080},
+  year={2020}
+}
+```
+
+### Contributions
+
+Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.

huggingface_dataset/Dataset_Card/ilist.md

@@ -0,0 +1,222 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- awa
+- bho
+- bra
+- hi
+- mag
+license:
+- cc-by-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids: []
+pretty_name: ilist
+tags:
+- language-identification
+dataset_info:
+  features:
+  - name: language_id
+    dtype:
+      class_label:
+        names:
+          '0': AWA
+          '1': BRA
+          '2': MAG
+          '3': BHO
+          '4': HIN
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 14362998
+    num_examples: 70351
+  - name: test
+    num_bytes: 2146857
+    num_examples: 9692
+  - name: validation
+    num_bytes: 2407643
+    num_examples: 10329
+  download_size: 18284850
+  dataset_size: 18917498
+---
+
+# Dataset Card for ilist
+
+## 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/kmi-linguistics/vardial2018
+- **Paper:** [Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign](https://aclanthology.org/W18-3901/)
+- **Leaderboard:**
+- **Point of Contact:** linguistics.kmi@gmail.com
+
+### Dataset Summary
+
+This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family: Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri and Magahi. These languages form part of a continuum starting from Western Uttar Pradesh (Hindi and Braj Bhasha) to Eastern Uttar Pradesh (Awadhi and Bhojpuri) and the neighbouring Eastern state of Bihar (Bhojpuri and Magahi).
+
+For this task, participants were provided with a dataset of approximately 15,000 sentences in each language, mainly from the domain of literature, published over the web as well as in print.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+ Hindi, Braj Bhasha, Awadhi, Bhojpuri and Magahi
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{
+    "language_id": 4,
+    "text": 'तभी बारिश हुई थी जिसका गीलापन इन मूर्तियों को इन तस्वीरों में एक अलग रूप देता है .'
+}
+```
+
+### Data Fields
+
+- `text`: text which you want to classify
+- `language_id`: label for the text as an integer from 0 to 4
+The language ids correspond to the following languages: "AWA", "BRA", "MAG", "BHO", "HIN".
+
+### Data Splits
+
+|                      | train | valid | test  |
+|----------------------|-------|-------|-------|
+| # of input sentences | 70351 | 9692  | 10329 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+The data for this task was collected from both hard printed and digital sources. Printed materials were
+obtained from different institutions that promote these languages. We also gathered data from libraries,
+as well as from local literary and cultural groups. We collected printed stories, novels and essays in
+books, magazines, and newspapers.
+
+#### Initial Data Collection and Normalization
+
+We scanned the printed materials, then we performed OCR, and
+finally we asked native speakers of the respective languages to correct the OCR output. Since there are
+no specific OCR models available for these languages, we used the Google OCR for Hindi, part of the
+Drive API. Since all the languages used the Devanagari script, we expected the OCR to work reasonably
+well, and overall it did. We further managed to get some blogs in Magahi and Bhojpuri.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+This work is licensed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/
+
+### Citation Information
+
+```
+@inproceedings{zampieri-etal-2018-language,
+    title = "Language Identification and Morphosyntactic Tagging: The Second {V}ar{D}ial Evaluation Campaign",
+    author = {Zampieri, Marcos  and
+      Malmasi, Shervin  and
+      Nakov, Preslav  and
+      Ali, Ahmed  and
+      Shon, Suwon  and
+      Glass, James  and
+      Scherrer, Yves  and
+      Samard{\v{z}}i{\'c}, Tanja  and
+      Ljube{\v{s}}i{\'c}, Nikola  and
+      Tiedemann, J{\"o}rg  and
+      van der Lee, Chris  and
+      Grondelaers, Stefan  and
+      Oostdijk, Nelleke  and
+      Speelman, Dirk  and
+      van den Bosch, Antal  and
+      Kumar, Ritesh  and
+      Lahiri, Bornini  and
+      Jain, Mayank},
+    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
+    month = aug,
+    year = "2018",
+    address = "Santa Fe, New Mexico, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W18-3901",
+    pages = "1--17",
+}
+```
+
+### Contributions
+
+Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.

huggingface_dataset/Dataset_Card/mesolitica_noisy-ms-en-augmentation.md

@@ -0,0 +1,14 @@
+---
+tags:
+- generated_from_keras_callback
+model-index:
+- name: t5-tiny-finetuned-noisy-ms-en
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# ms-en
+
+Notebooks to gather the dataset at https://github.com/huseinzol05/malay-dataset/tree/master/translation/noisy-ms-en-augmentation

huggingface_dataset/Dataset_Card/mutual_friends.md

@@ -0,0 +1,302 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- dialogue-modeling
+paperswithcode_id: mutualfriends
+pretty_name: MutualFriends
+dataset_info:
+  features:
+  - name: uuid
+    dtype: string
+  - name: scenario_uuid
+    dtype: string
+  - name: scenario_alphas
+    sequence: float32
+  - name: scenario_attributes
+    sequence:
+    - name: unique
+      dtype: bool_
+    - name: value_type
+      dtype: string
+    - name: name
+      dtype: string
+  - name: scenario_kbs
+    sequence:
+      sequence:
+        sequence:
+          sequence: string
+  - name: agents
+    struct:
+    - name: '1'
+      dtype: string
+    - name: '0'
+      dtype: string
+  - name: outcome_reward
+    dtype: int32
+  - name: events
+    struct:
+    - name: actions
+      sequence: string
+    - name: start_times
+      sequence: float32
+    - name: data_messages
+      sequence: string
+    - name: data_selects
+      sequence:
+      - name: attributes
+        sequence: string
+      - name: values
+        sequence: string
+    - name: agents
+      sequence: int32
+    - name: times
+      sequence: float32
+  config_name: plain_text
+  splits:
+  - name: train
+    num_bytes: 26979472
+    num_examples: 8967
+  - name: test
+    num_bytes: 3327158
+    num_examples: 1107
+  - name: validation
+    num_bytes: 3267881
+    num_examples: 1083
+  download_size: 41274578
+  dataset_size: 33574511
+---
+
+# Dataset Card for MutualFriends
+
+## 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:** [COCOA](https://stanfordnlp.github.io/cocoa/)
+- **Repository:** [Github repository](https://github.com/stanfordnlp/cocoa)
+- **Paper:** [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (ACL 2017)](https://arxiv.org/abs/1704.07130)
+- **Codalab**: [Codalab](https://worksheets.codalab.org/worksheets/0xc757f29f5c794e5eb7bfa8ca9c945573/)
+
+### Dataset Summary
+
+Our goal is to build systems that collaborate with people by exchanging information through natural language and reasoning over structured knowledge base. In the MutualFriend task, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend.
+
+### Supported Tasks and Leaderboards
+
+We consider two agents, each with a private knowledge base of items, who must communicate their knowledge to achieve a common goal. Specifically, we designed the MutualFriends task (see the figure below). Each agent has a list of friends with attributes like school, major etc. They must chat with each other to find the unique mutual friend.
+
+### Languages
+
+The text in the dataset is in English. The associated BCP-47 code is `en`.
+
+## Dataset Structure
+
+### Data Instances
+
+An example looks like this.
+
+```
+{
+  'uuid': 'C_423324a5fff045d78bef75a6f295a3f4'
+
+  'scenario_uuid': 'S_hvmRM4YNJd55ecT5',
+  'scenario_alphas': [0.30000001192092896, 1.0, 1.0],
+  'scenario_attributes': {
+    'name': ['School', 'Company', 'Location Preference'],
+    'unique': [False, False, False],
+    'value_type': ['school', 'company', 'loc_pref']
+  },
+  'scenario_kbs': [
+    [
+      [['School', 'Company', 'Location Preference'], ['Longwood College', 'Alton Steel', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Leonard Green & Partners', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['New Mexico Highlands University', 'Crazy Eddie', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['Rhodes College', "Tully's Coffee", 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['Sacred Heart University', 'AMR Corporation', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Molycorp', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['New Mexico Highlands University', 'The Hartford Financial Services Group', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['Sacred Heart University', 'Molycorp', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['Babson College', 'The Hartford Financial Services Group', 'indoor']]
+    ],
+    [
+      [['School', 'Company', 'Location Preference'], ['National Technological University', 'Molycorp', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['Fairmont State College', 'Leonard Green & Partners', 'outdoor']],
+      [['School', 'Company', 'Location Preference'], ['Johnson C. Smith University', 'Data Resources Inc.', 'outdoor']],
+      [['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Molycorp', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['Fairmont State College', 'Molycorp', 'outdoor']],
+      [['School', 'Company', 'Location Preference'], ['University of South Carolina - Aiken', 'Molycorp', 'indoor']],
+      [['School', 'Company', 'Location Preference'], ['University of South Carolina - Aiken', 'STX', 'outdoor']],
+      [['School', 'Company', 'Location Preference'], ['National Technological University', 'STX', 'outdoor']],
+      [['School', 'Company', 'Location Preference'], ['Johnson C. Smith University', 'Rockstar Games', 'indoor']]
+    ]
+  ],
+
+  'agents': {
+    '0': 'human',
+    '1': 'human'
+  },
+
+  'outcome_reward': 1,
+
+  'events': {
+    'actions': ['message', 'message', 'message', 'message', 'select', 'select'],
+    'agents': [1, 1, 0, 0, 1, 0],
+    'data_messages': ['Hello', 'Do you know anyone who works at Molycorp?', 'Hi. All of my friends like the indoors.', 'Ihave two friends that work at Molycorp. They went to Salisbury and Sacred Heart.', '', ''],
+    'data_selects': {
+      'attributes': [
+        [], [], [], [], ['School', 'Company', 'Location Preference'], ['School', 'Company', 'Location Preference']
+      ],
+      'values': [
+        [], [], [], [], ['Salisbury State University', 'Molycorp', 'indoor'], ['Salisbury State University', 'Molycorp', 'indoor']
+      ]
+    },
+    'start_times': [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0],
+    'times': [1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0]
+  },
+}
+```
+
+### Data Fields
+
+- `uuid`: example id.
+- `scenario_uuid`: scenario id.
+- `scenario_alphas`: scenario alphas.
+- `scenario_attributes`: all the attributes considered in the scenario. The dictionaries are liniearized: to reconstruct the dictionary of attribute i-th, one should extract the i-th elements of `unique`, `value_type` and `name`.
+  - `unique`: bool.
+  - `value_type`: code/type of the attribute.
+  - `name`: name of the attribute.
+- `scenario_kbs`: descriptions of the persons present in the two users' databases. List of two (one for each user in the dialogue). `scenario_kbs[i]` is a list of persons. Each person is represented as two lists (one for attribute names and the other for attribute values). The j-th element of attribute names corresponds to the j-th element of attribute values (linearized dictionary).
+- `agents`: the two users engaged in the dialogue.
+- `outcome_reward`: reward of the present dialogue.
+- `events`: dictionary describing the dialogue. The j-th element of each sub-element of the dictionary describes the turn along the axis of the sub-element.
+  - `actions`: type of turn (either `message` or `select`).
+  - `agents`: who is talking? Agent 1 or 0?
+  - `data_messages`: the string exchanged if `action==message`. Otherwise, empty string.
+  - `data_selects`: selection of the user if `action==select`. Otherwise, empty selection/dictionary.
+  - `start_times`: always -1 in these data.
+  - `times`: sending time.
+
+### Data Splits
+
+There are 8967 dialogues for training, 1083 for validation and 1107 for testing.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{he-etal-2017-learning,
+    title = "Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings",
+    author = "He, He  and
+      Balakrishnan, Anusha  and
+      Eric, Mihail  and
+      Liang, Percy",
+    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2017",
+    address = "Vancouver, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/P17-1162",
+    doi = "10.18653/v1/P17-1162",
+    pages = "1766--1776",
+    abstract = "We study a \textit{symmetric collaborative dialogue} setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.",
+}
+```
+
+### Contributions
+
+Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

huggingface_dataset/Dataset_Card/nateraw_beans.md

@@ -0,0 +1,165 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: Beans
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-image-classification
+---
+
+# Dataset Card for Beans
+
+## 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:**[Beans Homepage](https://github.com/AI-Lab-Makerere/ibean/)
+- **Repository:**[AI-Lab-Makerere/ibean](https://github.com/AI-Lab-Makerere/ibean/)
+- **Paper:** N/A
+- **Leaderboard:** N/A
+- **Point of Contact:** N/A
+
+### Dataset Summary
+
+Beans leaf dataset with images of diseased and health leaves.
+
+### Supported Tasks and Leaderboards
+
+- image-classification
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from the training set is provided below:
+
+```
+{
+    'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/0aaa78294d4bf5114f58547e48d91b7826649919505379a167decb629aa92b0a/train/bean_rust/bean_rust_train.109.jpg',
+    'labels': 1
+}
+```
+
+### Data Fields
+
+The data instances have the following fields:
+
+- `image_file_path`: a `string` filepath to an image.
+- `labels`: an `int` classification label.
+
+### Data Splits
+
+ 
+|   name   |train|validation|test|
+|----------|----:|----:|----:|
+|beans|1034|133|128|
+
+## 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
+
+```
+@ONLINE {beansdata,
+    author="Makerere AI Lab",
+    title="Bean disease dataset",
+    month="January",
+    year="2020",
+    url="https://github.com/AI-Lab-Makerere/ibean/"
+}
+```
+
+### Contributions
+
+Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.

huggingface_dataset/Dataset_Card/openslr.md

@@ -0,0 +1,1229 @@
+---
+pretty_name: OpenSLR
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- af
+- bn
+- ca
+- en
+- es
+- eu
+- gl
+- gu
+- jv
+- km
+- kn
+- ml
+- mr
+- my
+- ne
+- si
+- st
+- su
+- ta
+- te
+- tn
+- ve
+- xh
+- yo
+language_bcp47:
+- en-GB
+- en-IE
+- en-NG
+- es-CL
+- es-CO
+- es-PE
+- es-PR
+license:
+- cc-by-sa-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+paperswithcode_id: null
+configs:
+- SLR32
+- SLR35
+- SLR36
+- SLR41
+- SLR42
+- SLR43
+- SLR44
+- SLR52
+- SLR53
+- SLR54
+- SLR63
+- SLR64
+- SLR65
+- SLR66
+- SLR69
+- SLR70
+- SLR71
+- SLR72
+- SLR73
+- SLR74
+- SLR75
+- SLR76
+- SLR77
+- SLR78
+- SLR79
+- SLR80
+- SLR83
+- SLR86
+dataset_info:
+- config_name: SLR41
+  features:
+  - name: path
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 48000
+  - name: sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2423902
+    num_examples: 5822
+  download_size: 1890792360
+  dataset_size: 2423902
+- config_name: SLR42
+  features:
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+    dtype: string
+  - name: audio
+    dtype:
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+  - name: sentence
+    dtype: string
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+    num_examples: 2906
+  download_size: 866086951
+  dataset_size: 1427984
+- config_name: SLR43
+  features:
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+    dtype: string
+  - name: audio
+    dtype:
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+        sampling_rate: 48000
+  - name: sentence
+    dtype: string
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+    num_examples: 2064
+  download_size: 800375645
+  dataset_size: 1074005
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+  features:
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+    dtype: string
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+  - name: sentence
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+- config_name: SLR32
+  features:
+  - name: path
+    dtype: string
+  - name: audio
+    dtype:
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+  - name: sentence
+    dtype: string
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+  features:
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+  dataset_size: 77369899
+- config_name: SLR53
+  features:
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+    dtype: string
+  - name: audio
+    dtype:
+      audio:
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+  - name: sentence
+    dtype: string
+  splits:
+  - name: train
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+    num_examples: 218703
+  download_size: 14630810921
+  dataset_size: 88073248
+- config_name: SLR54
+  features:
+  - name: path
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 48000
+  - name: sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 62735822
+    num_examples: 157905
+  download_size: 9328247362
+  dataset_size: 62735822
+- config_name: SLR83
+  features:
+  - name: path
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 48000
+  - name: sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 7098985
+    num_examples: 17877
+  download_size: 7229890819
+  dataset_size: 7098985
+---
+
+# Dataset Card for openslr
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://www.openslr.org/
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, 
+and software related to speech recognition. Currently, following resources are available: 
+
+#### SLR32: High quality TTS data for four South African languages (af, st, tn, xh).
+This data set contains multi-speaker high quality transcribed audio data for four languages of South Africa.
+The data set consists of wave files, and a TSV file transcribing the audio. In each folder, the file line_index.tsv
+contains a FileID, which in turn contains the UserID and the Transcription of audio in the file.
+
+The data set has had some quality checks, but there might still be errors.
+
+This data set was collected by as a collaboration between North West University and Google.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See https://github.com/google/language-resources#license for license information.
+
+Copyright 2017 Google, Inc.
+
+#### SLR35: Large Javanese ASR training data set.
+This data set contains transcribed audio data for Javanese (~185K utterances). The data set consists of wave files,
+and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada 
+in Indonesia.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/35/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017 Google, Inc.
+
+#### SLR36: Large Sundanese ASR training data set.
+This data set contains transcribed audio data for Sundanese (~220K utterances). The data set consists of wave files,
+and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+This dataset was collected by Google in Indonesia.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/36/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017 Google, Inc.
+
+#### SLR41: High quality TTS data for Javanese.
+This data set contains high-quality transcribed audio data for Javanese. The data set consists of wave files, 
+and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each 
+filename is prepended with a speaker identification number.
+
+The data set has been manually quality checked, but there might still be errors.
+
+This dataset was collected by Google in collaboration with Gadjah Mada University in Indonesia.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/41/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017, 2018 Google LLC
+
+#### SLR42: High quality TTS data for Khmer.
+This data set contains high-quality transcribed audio data for Khmer. The data set consists of wave files, 
+and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. 
+Each filename is prepended with a speaker identification number.
+
+The data set has been manually quality checked, but there might still be errors.
+
+This dataset was collected by Google.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/42/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017, 2018 Google LLC
+
+#### SLR43: High quality TTS data for Nepali.
+This data set contains high-quality transcribed audio data for Nepali. The data set consists of wave files, 
+and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. 
+Each filename is prepended with a speaker identification number.
+
+The data set has been manually quality checked, but there might still be errors.
+
+This dataset was collected by Google in Nepal.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/43/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017, 2018 Google LLC
+
+#### SLR44: High quality TTS data for Sundanese.
+This data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, 
+and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. 
+Each filename is prepended with a speaker identification number.
+
+The data set has been manually quality checked, but there might still be errors.
+
+This dataset was collected by Google in collaboration with Universitas Pendidikan Indonesia.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/44/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017, 2018 Google LLC
+
+#### SLR52: Large Sinhala ASR training data set.
+This data set contains transcribed audio data for Sinhala (~185K utterances). The data set consists of wave files,
+and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/52/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017, 2018 Google, Inc.
+
+#### SLR53: Large Bengali ASR training data set.
+This data set contains transcribed audio data for Bengali (~196K utterances). The data set consists of wave files,
+and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/53/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017, 2018 Google, Inc.
+
+#### SLR54: Large Nepali ASR training data set.
+This data set contains transcribed audio data for Nepali (~157K utterances). The data set consists of wave files,
+and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/54/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2016, 2017, 2018 Google, Inc.
+
+#### SLR63: Crowdsourced high-quality Malayalam multi-speaker speech data set
+This data set contains transcribed high-quality audio of Malayalam sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/63/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR64: Crowdsourced high-quality Marathi multi-speaker speech data set
+This data set contains transcribed high-quality audio of Marathi sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/64/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+#### SLR65: Crowdsourced high-quality Tamil multi-speaker speech data set
+This data set contains transcribed high-quality audio of Tamil sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/65/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR66: Crowdsourced high-quality Telugu multi-speaker speech data set
+This data set contains transcribed high-quality audio of Telugu sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/66/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR69: Crowdsourced high-quality Catalan multi-speaker speech data set
+This data set contains transcribed high-quality audio of Catalan sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/69/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR70: Crowdsourced high-quality Nigerian English speech data set
+This data set contains transcribed high-quality audio of Nigerian English sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/70/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR71: Crowdsourced high-quality Chilean Spanish speech data set
+This data set contains transcribed high-quality audio of Chilean Spanish sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/71/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR72: Crowdsourced high-quality Colombian Spanish speech data set
+This data set contains transcribed high-quality audio of Colombian Spanish sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/72/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR73: Crowdsourced high-quality Peruvian Spanish speech data set
+This data set contains transcribed high-quality audio of Peruvian Spanish sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/73/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR74: Crowdsourced high-quality Puerto Rico Spanish speech data set
+This data set contains transcribed high-quality audio of Puerto Rico Spanish sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/74/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR75: Crowdsourced high-quality Venezuelan Spanish speech data set
+This data set contains transcribed high-quality audio of Venezuelan Spanish sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/75/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR76: Crowdsourced high-quality Basque speech data set
+This data set contains transcribed high-quality audio of Basque sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/76/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR77: Crowdsourced high-quality Galician speech data set
+This data set contains transcribed high-quality audio of Galician sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/77/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR78: Crowdsourced high-quality Gujarati multi-speaker speech data set
+This data set contains transcribed high-quality audio of Gujarati sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/78/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR79: Crowdsourced high-quality Kannada multi-speaker speech data set
+This data set contains transcribed high-quality audio of Kannada sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/79/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR80: Crowdsourced high-quality Burmese speech data set
+This data set contains transcribed high-quality audio of Burmese sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/80/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR83: Crowdsourced high-quality UK and Ireland English Dialect speech data set
+This data set contains transcribed high-quality audio of English sentences recorded by volunteers speaking different dialects of the language.
+The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.csv contains a line id, an anonymized FileID and the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+The recordings from the Welsh English speakers were collected in collaboration with Cardiff University.
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/83/LICENSE) file and https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019 Google, Inc.
+
+#### SLR86: Crowdsourced high-quality  multi-speaker speech data set
+This data set contains transcribed high-quality audio of  sentences recorded by volunteers. The data set 
+consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and 
+the transcription of audio in the file.
+
+The data set has been manually quality checked, but there might still be errors.
+
+Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
+
+The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
+See [LICENSE](https://www.openslr.org/resources/86/LICENSE) file and 
+https://github.com/google/language-resources#license for license information.
+
+Copyright 2018, 2019, 2020 Google, Inc.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+Javanese, Khmer, Nepali, Sundanese, Malayalam, Marathi, Tamil, Telugu, Catalan, Nigerian English, Chilean Spanish,
+Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati, Kannada,
+Afrikaans, Sesotho, Setswana and isiXhosa.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises the path to the audio file, called path and its sentence. 
+
+#### SLR32, SLR35, SLR36, SLR41, SLR42, SLR43, SLR44, SLR52, SLR53, SLR54, SLR63, SLR64, SLR65, SLR66, SLR69, SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80, SLR86
+```
+{
+  'path': '/home/cahya/.cache/huggingface/datasets/downloads/extracted/4d9cf915efc21110199074da4d492566dee6097068b07a680f670fcec9176e62/su_id_female/wavs/suf_00297_00037352660.wav'
+  'audio': {'path': '/home/cahya/.cache/huggingface/datasets/downloads/extracted/4d9cf915efc21110199074da4d492566dee6097068b07a680f670fcec9176e62/su_id_female/wavs/suf_00297_00037352660.wav',
+	  'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346,
+			  0.00091553,  0.00085449], dtype=float32),
+	  'sampling_rate': 16000},
+  'sentence': 'Panonton ting haruleng ningali Kelly Clarkson keur nyanyi di tipi',
+}
+```
+
+### Data Fields
+
+- `path`: The path to the audio file.
+- `audio`: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling
+  rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and
+  resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might
+  take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column,
+  *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+- `sentence`: The sentence the user was prompted to speak.
+
+### Data Splits
+
+There is only one "train" split for all configurations and the number of examples are:
+
+|       |   Number of examples |
+|:------|---------------------:|
+| SLR41 |                 5822 |
+| SLR42 |                 2906 |
+| SLR43 |                 2064 |
+| SLR44 |                 4213 |
+| SLR63 |                 4126 |
+| SLR64 |                 1569 |
+| SLR65 |                 4284 |
+| SLR66 |                 4448 |
+| SLR69 |                 4240 |
+| SLR35 |               185076 |
+| SLR36 |               219156 |
+| SLR70 |                 3359 |
+| SLR71 |                 4374 |
+| SLR72 |                 4903 |
+| SLR73 |                 5447 |
+| SLR74 |                  617 |
+| SLR75 |                 3357 |
+| SLR76 |                 7136 |
+| SLR77 |                 5587 |
+| SLR78 |                 4272 |
+| SLR79 |                 4400 |
+| SLR80 |                 2530 |
+| SLR86 |                 3583 |
+| SLR32 |                 9821 |
+| SLR52 |               185293 |
+| SLR53 |               218703 |
+| SLR54 |               157905 |
+| SLR83 |                17877 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[More Information Needed] 
+
+### Other Known Limitations
+
+[More Information Needed] 
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed] 
+
+### Licensing Information
+
+Each dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License ([CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)).
+See https://github.com/google/language-resources#license or the resource page on [OpenSLR](https://openslr.org/resources.php) for more information.
+
+### Citation Information
+#### SLR32
+```
+@inproceedings{van-niekerk-etal-2017,
+    title = {{Rapid development of TTS corpora for four South African languages}},
+    author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson and Martin Jansche and Linne Ha},
+    booktitle = {Proc. Interspeech 2017},
+    pages = {2178--2182},
+    address = {Stockholm, Sweden},
+    month = aug,
+    year  = {2017},
+    URL   = {https://dx.doi.org/10.21437/Interspeech.2017-1139}
+}
+```
+
+#### SLR35, SLR36, SLR52, SLR53, SLR54
+```
+@inproceedings{kjartansson-etal-sltu2018,
+    title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese,  Sinhala, Nepali, and Bangladeshi Bengali}},
+    author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
+    booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
+    year  = {2018},
+    address = {Gurugram, India},
+    month = aug,
+    pages = {52--55},
+    URL   = {https://dx.doi.org/10.21437/SLTU.2018-11},
+}
+```
+
+#### SLR41, SLR42, SLR43, SLR44 
+```
+@inproceedings{kjartansson-etal-tts-sltu2018,
+    title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},
+    author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin},
+    booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
+    year  = {2018},
+    address = {Gurugram, India},
+    month = aug,
+    pages = {66--70},
+    URL   = {https://dx.doi.org/10.21437/SLTU.2018-14}
+}
+```
+
+#### SLR63, SLR64, SLR65, SLR66, SLR78, SLR79
+```
+@inproceedings{he-etal-2020-open,
+  title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}},
+  author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot},
+  booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
+  month = may,
+  year = {2020},
+  address = {Marseille, France},
+  publisher = {European Language Resources Association (ELRA)},
+  pages = {6494--6503},
+  url = {https://www.aclweb.org/anthology/2020.lrec-1.800},
+  ISBN = "{979-10-95546-34-4},
+}
+```
+
+#### SLR69, SLR76, SLR77
+```
+@inproceedings{kjartansson-etal-2020-open,
+    title = {{Open-Source High Quality Speech Datasets for Basque, Catalan and Galician}},
+    author = {Kjartansson, Oddur and Gutkin, Alexander and Butryna, Alena and Demirsahin, Isin and Rivera, Clara},
+    booktitle = {Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)},
+    year = {2020},
+    pages = {21--27},
+    month = may,
+    address = {Marseille, France},
+    publisher = {European Language Resources association (ELRA)},
+    url = {https://www.aclweb.org/anthology/2020.sltu-1.3},
+    ISBN = {979-10-95546-35-1},
+}
+```
+
+#### SLR70, SLR71, SLR72, SLR73, SLR74, SLR75
+```
+@inproceedings{guevara-rukoz-etal-2020-crowdsourcing,
+    title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}},
+    author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur},
+    booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
+    year = {2020},
+    month = may,
+    address = {Marseille, France},
+    publisher = {European Language Resources Association (ELRA)},
+    url = {https://www.aclweb.org/anthology/2020.lrec-1.801},
+    pages = {6504--6513},
+    ISBN = {979-10-95546-34-4},
+}
+```
+
+#### SLR80
+```
+@inproceedings{oo-etal-2020-burmese,
+    title = {{Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech}},
+    author = {Oo, Yin May and Wattanavekin, Theeraphol and Li, Chenfang and De Silva, Pasindu and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Kjartansson, Oddur and Gutkin, Alexander},
+    booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
+    month = may,
+    year = {2020},
+    pages = "6328--6339",
+    address = {Marseille, France},
+    publisher = {European Language Resources Association (ELRA)},
+    url = {https://www.aclweb.org/anthology/2020.lrec-1.777},
+    ISBN = {979-10-95546-34-4},
+}
+```
+
+#### SLR86
+```
+@inproceedings{gutkin-et-al-yoruba2020,
+    title = {{Developing an Open-Source Corpus of Yoruba Speech}},
+    author = {Alexander Gutkin and I{\c{s}}{\i}n Demir{\c{s}}ahin and Oddur Kjartansson and Clara Rivera and K\d{\'o}lá Túb\d{\`o}sún},
+    booktitle = {Proceedings of Interspeech 2020},
+    pages = {404--408},
+    month = {October},
+    year = {2020},
+    address = {Shanghai, China},
+    publisher = {International Speech and Communication Association (ISCA)},
+    doi = {10.21437/Interspeech.2020-1096},
+    url = {https://dx.doi.org/10.21437/Interspeech.2020-1096},
+}
+```
+### Contributions
+
+Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.

huggingface_dataset/Dataset_Card/pauli31_czech-subjectivity-dataset.md

@@ -0,0 +1,67 @@
+---
+annotations_creators: []
+language_creators: []
+language:
+- cs-CZ
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: Czech Subjectivity Dataset
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for Czech Subjectivity Dataset
+
+
+### Dataset Summary
+
+Czech subjectivity dataset (Subj-CS) of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. See the paper description https://arxiv.org/abs/2204.13915
+
+
+### Github
+https://github.com/pauli31/czech-subjectivity-dataset
+
+### Supported Tasks and Leaderboards
+
+Subjectivity Analysis
+
+### Languages
+
+Czech
+
+### Data Instances
+
+train/dev/test
+
+### Licensing Information
+
+[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+### Citation Information
+
+If you use our dataset or software for academic research, please cite the our [paper](https://arxiv.org/abs/2204.13915)
+
+```
+@article{pib2022czech,
+    title={Czech Dataset for Cross-lingual Subjectivity Classification},
+    author={Pavel Přibáň and Josef Steinberger},
+    year={2022},
+    eprint={2204.13915},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+### Contact
+pribanp@kiv.zcu.cz
+
+### Contributions
+
+Thanks to [@pauli31](https://github.com/pauli31) for adding this dataset.

huggingface_dataset/Dataset_Card/roman_urdu.md

@@ -0,0 +1,181 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- ur
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+paperswithcode_id: roman-urdu-data-set
+pretty_name: Roman Urdu Dataset
+dataset_info:
+  features:
+  - name: sentence
+    dtype: string
+  - name: sentiment
+    dtype:
+      class_label:
+        names:
+          '0': Positive
+          '1': Negative
+          '2': Neutral
+  splits:
+  - name: train
+    num_bytes: 1633423
+    num_examples: 20229
+  download_size: 1628349
+  dataset_size: 1633423
+---
+
+# Dataset Card for Roman Urdu Dataset
+
+## 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:** [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set)
+- **Point of Contact:** [Zareen Sharf](mailto:zareensharf76@gmail.com)
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Urdu
+
+## Dataset Structure
+
+[More Information Needed]
+
+### Data Instances
+
+```
+Wah je wah,Positive,
+```
+
+### Data Fields
+
+Each row consists of a short Urdu text, followed by a sentiment label. The labels are one of `Positive`, `Negative`, and `Neutral`. Note that the original source file is a comma-separated values file.
+
+* `sentence`: A short Urdu text
+* `label`: One of `Positive`, `Negative`, and `Neutral`, indicating the polarity of the sentiment expressed in the sentence
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### 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
+
+```
+@InProceedings{Sharf:2018,
+  title     = "Performing Natural Language Processing on Roman Urdu Datasets",
+  authors   = "Zareen Sharf and Saif Ur Rahman",
+  booktitle = "International Journal of Computer Science and Network Security",
+  volume    = "18",
+  number    = "1",
+  pages     = "141-148",
+  year      = "2018"
+}
+
+@misc{Dua:2019,
+  author      = "Dua, Dheeru and Graff, Casey",
+  year        = "2017",
+  title       = "{UCI} Machine Learning Repository",
+  url         = "http://archive.ics.uci.edu/ml",
+  institution = "University of California, Irvine, School of Information and Computer Sciences"
+}
+```
+
+### Contributions
+
+Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset.

huggingface_dataset/Dataset_Card/tuple_ie.md

@@ -0,0 +1,252 @@
+---
+annotations_creators:
+- found
+language_creators:
+- machine-generated
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- other
+task_ids: []
+paperswithcode_id: tupleinf-open-ie-dataset
+pretty_name: TupleInf Open IE
+tags:
+- open-information-extraction
+dataset_info:
+- config_name: all
+  features:
+  - name: sentence
+    dtype: string
+  - name: tuples
+    sequence:
+    - name: score
+      dtype: float32
+    - name: tuple_text
+      dtype: string
+    - name: context
+      dtype: string
+    - name: arg1
+      dtype: string
+    - name: rel
+      dtype: string
+    - name: arg2s
+      sequence: string
+  splits:
+  - name: train
+    num_bytes: 115621096
+    num_examples: 267719
+  download_size: 18026102
+  dataset_size: 115621096
+- config_name: 4th_grade
+  features:
+  - name: sentence
+    dtype: string
+  - name: tuples
+    sequence:
+    - name: score
+      dtype: float32
+    - name: tuple_text
+      dtype: string
+    - name: context
+      dtype: string
+    - name: arg1
+      dtype: string
+    - name: rel
+      dtype: string
+    - name: arg2s
+      sequence: string
+  splits:
+  - name: train
+    num_bytes: 65363445
+    num_examples: 158910
+  download_size: 18026102
+  dataset_size: 65363445
+- config_name: 8th_grade
+  features:
+  - name: sentence
+    dtype: string
+  - name: tuples
+    sequence:
+    - name: score
+      dtype: float32
+    - name: tuple_text
+      dtype: string
+    - name: context
+      dtype: string
+    - name: arg1
+      dtype: string
+    - name: rel
+      dtype: string
+    - name: arg2s
+      sequence: string
+  splits:
+  - name: train
+    num_bytes: 50257651
+    num_examples: 108809
+  download_size: 18026102
+  dataset_size: 50257651
+---
+
+# Dataset Card for TupleInf Open IE
+
+## 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:** [Tuple IE Homepage](https://allenai.org/data/tuple-ie)
+- **Repository:**
+- **Paper:** [Answering Complex Questions Using Open Information Extraction](https://www.semanticscholar.org/paper/Answering-Complex-Questions-Using-Open-Information-Khot-Sabharwal/0ff595f0645a3e25a2f37145768985b10ead0509)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The text in the dataset is in English, collected from a large Web corpus using training questions from 4th and 8th grade as queries.
+
+## Dataset Structure
+
+### Data Instances
+
+This dataset contains setences with corresponding relation tuples extracted from each sentence. Each instance should contain a sentence and followed by the [Open IE v4](https://github.com/allenai/openie-standalone) tuples using their *simple format*.
+An example of an instance:
+
+```JSON
+{
+  "sentence": "0.04593 kg Used a triple beam balance to mass a golf ball.",
+  "tuples": {
+    "score": 0.8999999761581421,
+    "tuple_text": "(0.04593 kg; Used; a triple beam balance; to mass a golf ball)",
+    "context": "",
+    "arg1": "0.04593 kg",
+    "rel": "Used",
+    "arg2s": ["a triple beam balance", "to mass a golf ball"],
+  }
+}
+```
+
+### Data Fields
+
+- `sentence`: the input text/sentence.
+- `tuples`: the extracted relation tuples from the sentence.
+  - `score`: the confident score for each tuple.
+  - `tuple_text`: the relationship representation text of the extraction, in the *simple format* of [Open IE v4](https://github.com/allenai/openie-standalone).
+  - `context`: an optional representation of the context for this extraction. Defaults to `""` if there's no context.
+  - `arg1`: the first argument in the relationship.
+  - `rel`: the relation.
+  - `arg2s`: a sequence of the 2nd arguments in the realtionship.
+
+### Data Splits
+
+|    name   | train|
+|-----------|-----:|
+|    all    |267719|
+| 4th_grade |158910|
+| 8th_grade |108809|
+
+## 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
+
+```bibtex
+@article{Khot2017AnsweringCQ,
+  title={Answering Complex Questions Using Open Information Extraction},
+  author={Tushar Khot and A. Sabharwal and Peter Clark},
+  journal={ArXiv},
+  year={2017},
+  volume={abs/1704.05572}
+}
+```
+
+### Contributions
+
+Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.

huggingface_dataset/Dataset_Card/zoheb_sketch-scene.md

@@ -0,0 +1,40 @@
+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+language_creators:
+- machine-generated
+multilinguality:
+- monolingual
+pretty_name: 'Sketch Scene Descriptions'
+size_categories:
+- n<10K
+source_datasets:
+- FS-COCO
+tags: []
+task_categories:
+- text-to-image
+task_ids: []
+---
+
+# Dataset Card for Sketch Scene Descriptions
+
+_Dataset used to train [Sketch Scene text to image model]()_
+
+We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.
+
+For each row, the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
+
+
+## Citation
+
+If you use this dataset, please cite it as:
+
+```
+@inproceedings{fscoco,
+    title={FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context.}
+    author={Chowdhury, Pinaki Nath and Sain, Aneeshan and Bhunia, Ayan Kumar and Xiang, Tao and Gryaditskaya, Yulia and Song, Yi-Zhe},
+    booktitle={ECCV},
+    year={2022}
+}
+```