Upload batch 218 (20 files, last=huggingface_dataset/Dataset_Card/saibo_bookcorpus_small_compact_1024_n7.md)

huggingface_dataset/Dataset_Card/ARTeLab_ilpost.md

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+---
+language:
+- it
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100k
+task_categories:
+- summarization
+---
+
+# Dataset Card for ilpost
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+IlPost dataset, containing news articles taken from IlPost.
+
+There are two features:
+
+- source: Input news article.
+- target: Summary of the article.
+
+### Supported Tasks and Leaderboards
+
+- `abstractive-summarization`, `summarization`
+
+### Languages
+
+The text in the dataset is in Italian
+
+## Dataset Structure
+
+### Data Instances
+
+[Needs More Information]
+
+### Data Fields
+
+[Needs More Information]
+
+### Data Splits
+
+[Needs More Information]
+
+## 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
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
+
+```
+@Article{info13050228,
+    AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
+    TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
+    JOURNAL = {Information},
+    VOLUME = {13},
+    YEAR = {2022},
+    NUMBER = {5},
+    ARTICLE-NUMBER = {228},
+    URL = {https://www.mdpi.com/2078-2489/13/5/228},
+    ISSN = {2078-2489},
+    ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
+    DOI = {10.3390/info13050228}
+}
+```

huggingface_dataset/Dataset_Card/GEM_CrossWOZ.md

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+---
+annotations_creators:
+- none
+language_creators:
+- unknown
+language:
+- zh
+license:
+- apache-2.0
+multilinguality:
+- unknown
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- conversational
+task_ids: []
+pretty_name: CrossWOZ
+tags:
+- dialog-response-generation
+---
+
+# Dataset Card for GEM/CrossWOZ
+
+## Dataset Description
+
+- **Homepage:** https://github.com/thu-coai/CrossWOZ
+- **Repository:** https://github.com/thu-coai/CrossWOZ
+- **Paper:** https://aclanthology.org/2020.tacl-1.19
+- **Leaderboard:** N/A
+- **Point of Contact:** Qi Zhu
+
+### Link to Main Data Card
+
+You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/CrossWOZ).
+
+### Dataset Summary 
+
+CrossWOZ is a Chinese multi-domain task-oriented dialogue dataset . It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation.
+
+You can load the dataset via:
+```
+import datasets
+data = datasets.load_dataset('GEM/CrossWOZ')
+```
+The data loader can be found [here](https://huggingface.co/datasets/GEM/CrossWOZ).
+
+#### website
+[Github](https://github.com/thu-coai/CrossWOZ)
+
+#### paper
+[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)
+
+#### authors
+Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University
+
+## Dataset Overview
+
+### Where to find the Data and its Documentation
+
+#### Webpage
+
+<!-- info: What is the webpage for the dataset (if it exists)? -->
+<!-- scope: telescope -->
+[Github](https://github.com/thu-coai/CrossWOZ)
+
+#### Download
+
+<!-- info: What is the link to where the original dataset is hosted? -->
+<!-- scope: telescope -->
+[Github](https://github.com/thu-coai/CrossWOZ)
+
+#### Paper
+
+<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
+<!-- scope: telescope -->
+[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)
+
+#### BibTex
+
+<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
+<!-- scope: microscope -->
+```
+@article{zhu-etal-2020-crosswoz,
+    title = "{C}ross{WOZ}: A Large-Scale {C}hinese Cross-Domain Task-Oriented Dialogue Dataset",
+    author = "Zhu, Qi  and
+      Huang, Kaili  and
+      Zhang, Zheng  and
+      Zhu, Xiaoyan  and
+      Huang, Minlie",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "8",
+    year = "2020",
+    url = "https://aclanthology.org/2020.tacl-1.19",
+    doi = "10.1162/tacl_a_00314",
+    pages = "281--295",
+    abstract = "To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.",
+}
+```
+
+#### Contact Name
+
+<!-- quick -->
+<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+Qi Zhu
+
+#### Contact Email
+
+<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+zhuq96@gmail.com
+
+#### Has a Leaderboard?
+
+<!-- info: Does the dataset have an active leaderboard? -->
+<!-- scope: telescope -->
+no
+
+
+### Languages and Intended Use
+
+#### Multilingual?
+
+<!-- quick -->
+<!-- info: Is the dataset multilingual? -->
+<!-- scope: telescope -->
+no
+
+#### Covered Languages
+
+<!-- quick -->
+<!-- info: What languages/dialects are covered in the dataset? -->
+<!-- scope: telescope -->
+`Chinese`
+
+#### License
+
+<!-- quick -->
+<!-- info: What is the license of the dataset? -->
+<!-- scope: telescope -->
+apache-2.0: Apache License 2.0
+
+#### Intended Use
+
+<!-- info: What is the intended use of the dataset? -->
+<!-- scope: microscope -->
+CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. We also provide a user simulator and several benchmark models for pipelined taskoriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus.
+
+#### Primary Task
+
+<!-- info: What primary task does the dataset support? -->
+<!-- scope: telescope -->
+Dialog Response Generation
+
+#### Communicative Goal
+
+<!-- quick -->
+<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
+<!-- scope: periscope -->
+Generate a response according to the dialog context and database search results.
+
+
+### Credit
+
+#### Curation Organization Type(s)
+
+<!-- info: In what kind of organization did the dataset curation happen? -->
+<!-- scope: telescope -->
+`academic`
+
+#### Curation Organization(s)
+
+<!-- info: Name the organization(s). -->
+<!-- scope: periscope -->
+Tsinghua University
+
+#### Dataset Creators
+
+<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
+<!-- scope: microscope -->
+Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University
+
+#### Funding
+
+<!-- info: Who funded the data creation? -->
+<!-- scope: microscope -->
+National Science Foundation of China, National Key R&D Program of China 
+
+#### Who added the Dataset to GEM?
+
+<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
+<!-- scope: microscope -->
+Qi Zhu (Tsinghua University)
+
+
+### Dataset Structure
+
+#### Data Fields
+
+<!-- info: List and describe the fields present in the dataset. -->
+<!-- scope: telescope -->
+- `gem_id` (string): GEM-CrossWOZ-{split}-{id}
+- `dialog_id` (string): dialog ID
+- `sys_id` (string): system annotator ID
+- `usr_id` (string): user annotation ID
+- `type` (string): dialog type
+- `task description` (list of strings): natural language descriptions of the user goal
+- `goal` (list of tuples), includes:
+  - `sub-goal id` (string)
+  - `domain name` (string)
+  - `slot name` (string)
+  - `constraint` if filled, else `requirement` (string)
+  - `whether be mentioned in previous turns` (string)
+- `messages` (list of dict): dialog turns. Each turn contains:
+  - `content` (string): utterance
+  - `role` (string): user or system
+  - `dialog_act` (list of tuples), includes:
+    - `domain` (string)
+    - `intent` (string)
+    - `slot` (string)
+    - `value` (string)
+  - `user_state` (list of tuples): same format as "goal", can be viewed as dynamic goal.
+  - `sys_state_init` (dict): the first db query emitted, records user constraints faithfully. If the system find no result that matches, he/she may relax the constraints manually and search db multiple times.
+    - `domain` (dict): slot(string)-value(string) pairs
+    - `selectedResults` (list of string): db search result that would be used in this turn.
+  - `sys_state` (dict): the final db query emitted, records the db used by the system in this turn. Same format as sys_state_init. Note that this may not satisfy all user constraints.
+- `final_goal` (list of tuples): user state/goal at the end of dialog. same format as "goal".
+
+#### Example Instance
+
+<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
+<!-- scope: periscope -->
+```
+{'dialog_id': '2303',
+ 'final_goal': [['1', '餐馆', '人均消费', '50-100元', 'True'],
+  ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
+  ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
+  ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
+  ['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
+  ['2', '景点', '名称', '故宫', 'True'],
+  ['2', '景点', '评分', '4.5分以上', 'True'],
+  ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
+  ['2', '景点', '电话', '010-85007938', 'True'],
+  ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
+  ['3', '酒店', '电话', '010-84273030', 'True']],
+ 'gem_id': 'GEM-CrossWOZ-test-0',
+ 'goal': [['1', '餐馆', '人均消费', '50-100元', 'False'],
+  ['1', '餐馆', '推荐菜', "['美食街']", 'False'],
+  ['1', '餐馆', '名称', '', 'False'],
+  ['1', '餐馆', '营业时间', '', 'False'],
+  ['1', '餐馆', '周边景点', '[]', 'False'],
+  ['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
+  ['2', '景点', '评分', '4.5分以上', 'False'],
+  ['2', '景点', '地址', '', 'False'],
+  ['2', '景点', '电话', '', 'False'],
+  ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
+  ['3', '酒店', '电话', '', 'False']],
+ 'messages': {'content': ['你好���我想吃美食街，帮我推荐一个人均消费在50-100元的餐馆，谢谢。',
+   '为您推荐鲜鱼口老字号美食街，人均消费75元，有您想吃的美食街哦。',
+   '营业时间是什么时间？',
+   '周一至周日 10:00-22:00。',
+   '他家周边有什么景点吗？',
+   '有故宫, 前门大街, 恭王府, 天安门广场。',
+   '哦，我想在这些附近景点里找一个4.5分以上的，有吗？',
+   '故宫就是哦，4.7分。',
+   '好的，电话和地址告诉我一下。',
+   '010-85007938；北京市东城区景山前街4号。',
+   '好的，麻烦你帮我查一下桔子水晶酒店(北京安贞店)电话呗。',
+   '010-84273030。',
+   '好的，收到，谢谢你！',
+   '不客气。'],
+  'dialog_act': [[['General', 'greet', 'none', 'none'],
+    ['General', 'thank', 'none', 'none'],
+    ['Inform', '餐馆', '人均消费', '50-100元'],
+    ['Inform', '餐馆', '推荐菜', '美食街'],
+    ['Request', '餐馆', '名称', '']],
+   [['Inform', '餐馆', '人均消费', '75元'], ['Inform', '餐馆', '名称', '鲜鱼口老字号美食街']],
+   [['Request', '餐馆', '营业时间', '']],
+   [['Inform', '餐馆', '营业时间', '周一至周日 10:00-22:00']],
+   [['Request', '餐馆', '周边景点', '']],
+   [['Inform', '餐馆', '周边景点', '前门大街'],
+    ['Inform', '餐馆', '周边景点', '天安门广场'],
+    ['Inform', '餐馆', '周边景点', '恭王府'],
+    ['Inform', '餐馆', '周边景点', '故宫']],
+   [['Inform', '景点', '评分', '4.5分以上'], ['Select', '景点', '源领域', '餐馆']],
+   [['Inform', '景点', '名称', '故宫'], ['Inform', '景点', '评分', '4.7分']],
+   [['Request', '景点', '地址', ''], ['Request', '景点', '电话', '']],
+   [['Inform', '景点', '地址', '北京市东城区景山前街4号'],
+    ['Inform', '景点', '电话', '010-85007938']],
+   [['Inform', '酒店', '名称', '桔子水晶酒店(北京安贞店)'], ['Request', '酒店', '电话', '']],
+   [['Inform', '酒店', '电话', '010-84273030']],
+   [['General', 'thank', 'none', 'none']],
+   [['General', 'welcome', 'none', 'none']]],
+  'role': ['usr',
+   'sys',
+   'usr',
+   'sys',
+   'usr',
+   'sys',
+   'usr',
+   'sys',
+   'usr',
+   'sys',
+   'usr',
+   'sys',
+   'usr',
+   'sys'],
+  'sys_state': [{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '景点': {'selectedResults': [],
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '游玩时间': '',
+     '评分': '',
+     '门票': ''},
+    '酒店': {'selectedResults': [],
+     '价格': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '评分': '',
+     '酒店类型': '',
+     '酒店设施': ''},
+    '餐馆': {'selectedResults': [],
+     '人均消费': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '推荐菜': '',
+     '评分': ''}},
+   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '景点': {'selectedResults': [],
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '游玩时间': '',
+     '评分': '',
+     '门票': ''},
+    '酒店': {'selectedResults': [],
+     '价格': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '评分': '',
+     '酒店类型': '',
+     '酒店设施': ''},
+    '餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
+     '人均消费': '50-100元',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '推荐菜': '美食街',
+     '评分': ''}},
+   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '景点': {'selectedResults': [],
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '游玩时间': '',
+     '评分': '',
+     '门票': ''},
+    '酒店': {'selectedResults': [],
+     '价格': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '评分': '',
+     '酒店类型': '',
+     '酒店设施': ''},
+    '餐馆': {'selectedResults': [],
+     '人均消费': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '推荐菜': '',
+     '评分': ''}},
+   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '景点': {'selectedResults': [],
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '游玩时间': '',
+     '评分': '',
+     '门票': ''},
+    '酒店': {'selectedResults': [],
+     '价格': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '评分': '',
+     '酒店类型': '',
+     '酒店设施': ''},
+    '餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
+     '人均消费': '50-100元',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '推荐菜': '美食街',
+     '评分': ''}},
+   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '景点': {'selectedResults': [],
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '游玩时间': '',
+     '评分': '',
+     '门票': ''},
+    '酒店': {'selectedResults': [],
+     '价格': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
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+     '酒店类型': '',
+     '酒店设施': ''},
+    '餐馆': {'selectedResults': [],
+     '人均消费': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '推荐菜': '',
+     '评分': ''}},
+   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '景点': {'selectedResults': ['故宫'],
+     '名称': '故宫',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '游玩时间': '',
+     '评分': '',
+     '门票': ''},
+    '酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
+     '价格': '',
+     '名称': '桔子水晶酒店(北京安贞店)',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '评分': '',
+     '酒店类型': '',
+     '酒店设施': ''},
+    '餐馆': {'selectedResults': [],
+     '人均消费': '50-100元',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '推荐菜': '美食街',
+     '评分': ''}},
+   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '景点': {'selectedResults': [],
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '游玩时间': '',
+     '评分': '',
+     '门票': ''},
+    '酒店': {'selectedResults': [],
+     '价格': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '评分': '',
+     '酒店类型': '',
+     '酒店设施': ''},
+    '餐馆': {'selectedResults': [],
+     '人均消费': '',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '推荐菜': '',
+     '评分': ''}},
+   {'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
+    '景点': {'selectedResults': [],
+     '名称': '故宫',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '游玩时间': '',
+     '评分': '',
+     '门票': ''},
+    '酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
+     '价格': '',
+     '名称': '桔子水晶酒店(北京安贞店)',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '评分': '',
+     '酒店类型': '',
+     '酒店设施': ''},
+    '餐馆': {'selectedResults': [],
+     '人均消费': '50-100元',
+     '名称': '',
+     '周边景点': '',
+     '周边酒店': '',
+     '周边餐馆': '',
+     '推荐菜': '美食街',
+     '评分': ''}}],
+  'user_state': [[['1', '餐馆', '人均消费', '50-100元', 'True'],
+    ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
+    ['1', '餐馆', '名称', '', 'True'],
+    ['1', '餐馆', '营业时间', '', 'False'],
+    ['1', '餐馆', '周边景点', '[]', 'False'],
+    ['2', '景��', '名称', '出现在id=1的周边景点里', 'False'],
+    ['2', '景点', '评分', '4.5分以上', 'False'],
+    ['2', '景点', '地址', '', 'False'],
+    ['2', '景点', '电话', '', 'False'],
+    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
+    ['3', '酒店', '电话', '', 'False']],
+   [],
+   [['1', '餐馆', '人均消费', '50-100元', 'True'],
+    ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
+    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
+    ['1', '餐馆', '营业时间', '', 'True'],
+    ['1', '餐馆', '周边景点', '[]', 'False'],
+    ['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
+    ['2', '景点', '评分', '4.5分以上', 'False'],
+    ['2', '景点', '地址', '', 'False'],
+    ['2', '景点', '电话', '', 'False'],
+    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
+    ['3', '酒店', '电话', '', 'False']],
+   [],
+   [['1', '餐馆', '人均消费', '50-100元', 'True'],
+    ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
+    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
+    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
+    ['1', '餐馆', '周边景点', '[]', 'True'],
+    ['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
+    ['2', '景点', '评分', '4.5分以上', 'False'],
+    ['2', '景点', '地址', '', 'False'],
+    ['2', '景点', '电话', '', 'False'],
+    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
+    ['3', '酒店', '电话', '', 'False']],
+   [],
+   [['1', '餐馆', '人均消费', '50-100元', 'True'],
+    ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
+    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
+    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
+    ['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
+    ['2', '景点', '名称', '出现在id=1的周边景点里', 'True'],
+    ['2', '景点', '评分', '4.5分以上', 'True'],
+    ['2', '景点', '地址', '', 'False'],
+    ['2', '景点', '电话', '', 'False'],
+    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
+    ['3', '酒店', '电话', '', 'False']],
+   [],
+   [['1', '餐馆', '人均消费', '50-100元', 'True'],
+    ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
+    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
+    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
+    ['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
+    ['2', '景点', '名称', '故宫', 'True'],
+    ['2', '景点', '评分', '4.5分以上', 'True'],
+    ['2', '景点', '地址', '', 'True'],
+    ['2', '景点', '电话', '', 'True'],
+    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
+    ['3', '酒店', '电话', '', 'False']],
+   [],
+   [['1', '餐馆', '人均消费', '50-100元', 'True'],
+    ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
+    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
+    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
+    ['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
+    ['2', '景点', '名称', '故宫', 'True'],
+    ['2', '景点', '评分', '4.5分以上', 'True'],
+    ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
+    ['2', '景点', '电话', '010-85007938', 'True'],
+    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
+    ['3', '酒店', '电话', '', 'True']],
+   [],
+   [['1', '餐馆', '人均消费', '50-100元', 'True'],
+    ['1', '餐馆', '推荐菜', "['美食街']", 'True'],
+    ['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
+    ['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
+    ['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
+    ['2', '景点', '名称', '故宫', 'True'],
+    ['2', '景点', '评分', '4.5分以上', 'True'],
+    ['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
+    ['2', '景点', '电话', '010-85007938', 'True'],
+    ['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
+    ['3', '酒店', '电话', '010-84273030', 'True']],
+   []]},
+ 'sys_id': 96,
+ 'task description': ['你要去一个餐馆(id=1)用餐。你希望餐馆的人均消费是50-100元的。你想吃的菜肴是美食街。你想知道这个餐馆的名称、营业时间、周边景点。',
+  '你要去id=1附近的景点(id=2)游玩。你希望景点的评分是4.5分以上。你想知道这个景点的地址、电话。',
+  '你要去名叫桔子水晶酒店(北京安贞店)的酒店(id=3)住宿。你想知道这个酒店的电话。'],
+ 'type': '不独立多领域',
+ 'usr_id': 97}
+```
+
+#### Data Splits
+
+<!-- info: Describe and name the splits in the dataset if there are more than one. -->
+<!-- scope: periscope -->
+| Split                 | Train  | Valid | Test  |
+| --------------------- | ------ | ----- | ----- |
+| \# dialogues          | 5,012  | 500   | 500   |
+| \# Turns (utterances) | 84,692 | 8,458 | 8,476 |
+| Vocab                 | 12,502 | 5,202 | 5,143 |
+| Avg. sub-goals        | 3.24   | 3.26  | 3.26  |
+| Avg. semantic tuples  | 14.8   | 14.9  | 15.0  |
+| Avg. turns            | 16.9   | 16.9  | 17.0  |
+| Avg. tokens per turn  | 16.3   | 16.3  | 16.2  |
+
+
+
+## Dataset in GEM
+
+### Rationale for Inclusion in GEM
+
+#### Why is the Dataset in GEM?
+
+<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
+<!-- scope: microscope -->
+CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.
+
+#### Similar Datasets
+
+<!-- info: Do other datasets for the high level task exist? -->
+<!-- scope: telescope -->
+yes
+
+#### Unique Language Coverage
+
+<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
+<!-- scope: periscope -->
+no
+
+#### Difference from other GEM datasets
+
+<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
+<!-- scope: microscope -->
+The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.
+
+#### Ability that the Dataset measures
+
+<!-- info: What aspect of model ability can be measured with this dataset? -->
+<!-- scope: periscope -->
+Dialog understanding, dialog policy learning
+
+
+### GEM-Specific Curation
+
+#### Modificatied for GEM?
+
+<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
+<!-- scope: telescope -->
+yes
+
+#### GEM Modifications
+
+<!-- info: What changes have been made to he original dataset? -->
+<!-- scope: periscope -->
+`other`
+
+#### Modification Details
+
+<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
+<!-- scope: microscope -->
+To adapt to hugging face Datasets, we 1) separate user annotators' ID and system annotations' ID; 2) we convert the data type in goal/user state to string.
+
+#### Additional Splits?
+
+<!-- info: Does GEM provide additional splits to the dataset? -->
+<!-- scope: telescope -->
+no
+
+
+### Getting Started with the Task
+
+#### Pointers to Resources
+
+<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
+<!-- scope: microscope -->
+[Code](https://github.com/thu-coai/Convlab-2)
+
+#### Technical Terms
+
+<!-- info: Technical terms used in this card and the dataset and their definitions -->
+<!-- scope: microscope -->
+According to the type of user goal, we group the dialogues in the training set into five categories:
+- S: 417 dialogues have only one sub-goal in HAR domains.
+- M: 1573 dialogues have multiple sub-goals (2-3) in HAR domains. However, these sub-goals do not have cross-domain informable slots.
+- M+T: 691 dialogues have multiple sub-goals in HAR domains and at least one sub-goal in the metro or taxi domain (3-5 sub-goals). The sub-goals in HAR domains do not have cross-domain informable slots.
+- CM: 1,759 dialogues have multiple sub-goals (2-5) in HAR domains with cross-domain informable slots.
+- CM+T: 572 dialogues have multiple sub-goals in HAR domains with cross-domain informable slots and at least one sub-goal in the metro or taxi domain (3-5 sub-goals).
+
+
+
+
+## Previous Results
+
+### Previous Results
+
+#### Measured Model Abilities
+
+<!-- info: What aspect of model ability can be measured with this dataset? -->
+<!-- scope: telescope -->
+Dialog understanding, dialog policy learning
+
+#### Metrics
+
+<!-- info: What metrics are typically used for this task? -->
+<!-- scope: periscope -->
+`BLEU`
+
+#### Proposed Evaluation
+
+<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
+<!-- scope: microscope -->
+BLEU evaluates the generation quality.
+
+#### Previous results available?
+
+<!-- info: Are previous results available? -->
+<!-- scope: telescope -->
+yes
+
+#### Other Evaluation Approaches
+
+<!-- info: What evaluation approaches have others used? -->
+<!-- scope: periscope -->
+Inform rate: how many entities in the gold response appear in the generated response.
+
+#### Relevant Previous Results
+
+<!-- info: What are the most relevant previous results for this task/dataset? -->
+<!-- scope: microscope -->
+BLEU on MultiWOZ dataset.
+
+
+
+## Dataset Curation
+
+### Original Curation
+
+#### Original Curation Rationale
+
+<!-- info: Original curation rationale -->
+<!-- scope: telescope -->
+Gather human-to-human dialog in Chinese.
+
+#### Communicative Goal
+
+<!-- info: What was the communicative goal? -->
+<!-- scope: periscope -->
+Generate a response according to the dialog context and database search results.
+
+#### Sourced from Different Sources
+
+<!-- info: Is the dataset aggregated from different data sources? -->
+<!-- scope: telescope -->
+no
+
+
+### Language Data
+
+#### How was Language Data Obtained?
+
+<!-- info: How was the language data obtained? -->
+<!-- scope: telescope -->
+`Crowdsourced`
+
+#### Where was it crowdsourced?
+
+<!-- info: If crowdsourced, where from? -->
+<!-- scope: periscope -->
+`Participatory experiment`
+
+#### Language Producers
+
+<!-- info: What further information do we have on the language producers? -->
+<!-- scope: microscope -->
+An usr/sys ID indicates the creator of different data points.
+
+#### Topics Covered
+
+<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
+<!-- scope: periscope -->
+domains: attraction, hotel, restaurant, metro, taxi
+
+#### Data Validation
+
+<!-- info: Was the text validated by a different worker or a data curator? -->
+<!-- scope: telescope -->
+validated by data curator
+
+#### Was Data Filtered?
+
+<!-- info: Were text instances selected or filtered? -->
+<!-- scope: telescope -->
+not filtered
+
+
+### Structured Annotations
+
+#### Additional Annotations?
+
+<!-- quick -->
+<!-- info: Does the dataset have additional annotations for each instance? -->
+<!-- scope: telescope -->
+none
+
+#### Annotation Service?
+
+<!-- info: Was an annotation service used? -->
+<!-- scope: telescope -->
+no
+
+
+### Consent
+
+#### Any Consent Policy?
+
+<!-- info: Was there a consent policy involved when gathering the data? -->
+<!-- scope: telescope -->
+yes
+
+#### Consent Policy Details
+
+<!-- info: What was the consent policy? -->
+<!-- scope: microscope -->
+Annotators agree using the dataset for research purpose.
+
+#### Other Consented Downstream Use
+
+<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
+<!-- scope: microscope -->
+Any
+
+
+### Private Identifying Information (PII)
+
+#### Contains PII?
+
+<!-- quick -->
+<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
+<!-- scope: telescope -->
+unlikely
+
+#### Categories of PII
+
+<!-- info: What categories of PII are present or suspected in the data? -->
+<!-- scope: periscope -->
+`generic PII`
+
+#### Any PII Identification?
+
+<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
+<!-- scope: periscope -->
+no identification
+
+
+### Maintenance
+
+#### Any Maintenance Plan?
+
+<!-- info: Does the original dataset have a maintenance plan? -->
+<!-- scope: telescope -->
+no
+
+
+
+## Broader Social Context
+
+### Previous Work on the Social Impact of the Dataset
+
+#### Usage of Models based on the Data
+
+<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
+<!-- scope: telescope -->
+no
+
+
+### Impact on Under-Served Communities
+
+#### Addresses needs of underserved Communities?
+
+<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
+<!-- scope: telescope -->
+yes
+
+#### Details on how Dataset Addresses the Needs
+
+<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
+<!-- scope: microscope -->
+CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.
+
+
+### Discussion of Biases
+
+#### Any Documented Social Biases?
+
+<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
+<!-- scope: telescope -->
+no
+
+#### Are the Language Producers Representative of the Language?
+
+<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
+<!-- scope: periscope -->
+Yes
+
+
+
+## Considerations for Using the Data
+
+### PII Risks and Liability
+
+#### Potential PII Risk
+
+<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
+<!-- scope: microscope -->
+No
+
+
+### Licenses
+
+#### Copyright Restrictions on the Dataset
+
+<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
+<!-- scope: periscope -->
+`open license - commercial use allowed`
+
+#### Copyright Restrictions on the Language Data
+
+<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
+<!-- scope: periscope -->
+`open license - commercial use allowed`
+
+
+### Known Technical Limitations
+
+#### Technical Limitations
+
+<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
+<!-- scope: microscope -->
+No
+
+#### Unsuited Applications
+
+<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
+<!-- scope: microscope -->
+Model may not handle unknown values in the dialog
+
+#### Discouraged Use Cases
+
+<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
+<!-- scope: microscope -->
+Responses can be diverse, which is not captured by BLEU
+
+

huggingface_dataset/Dataset_Card/MLRS_masri_test.md

@@ -0,0 +1,149 @@
+---
+annotations_creators:
+- expert-generated
+language:
+- mt
+language_creators:
+- other
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: 'MASRI-TEST CORPUS: Audio and Transcriptions in Maltese extracted from the YouTube channel of the University of Malta.'
+size_categories:
+- n<1K
+source_datasets:
+- original
+tags:
+- masri
+- maltese
+- masri-project
+- malta
+- test corpus
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+---
+# Dataset Card for masri_test
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-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:** [MASRI Project](https://www.um.edu.mt/projects/masri/)
+- **Repository:** [MASRI Data Repo](https://github.com/UMSpeech/)
+- **Point of Contact:** [Carlos Mena](mailto:carlos.mena@ciempiess.org), [Andrea De Marco](mailto:andrea.demarco@um.edu.mt), [Claudia Borg](mailto:claudia.borg@um.edu.mt)
+### Dataset Summary
+The MASRI-TEST CORPUS was created out of YouTube videos belonging to the channel of the [University of Malta](www.youtube.com/user/universityofmalta). It has a length of 1 hour and it is gender balanced, as it has the same number of male and female speakers.
+### Example Usage
+The MASRI-TEST contains only the test split:
+```python
+from datasets import load_dataset
+masri_test = load_dataset("MLRS/masri_test")
+```
+It is also valid to do:
+```python
+from datasets import load_dataset
+masri_test = load_dataset("MLRS/masri_test",split="test")
+```
+### Supported Tasks
+automatic-speech-recognition: The dataset can be used to test a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
+### Languages
+The language of the corpus is Maltese.
+## Dataset Structure
+### Data Instances
+```python
+{
+  'audio_id': 'MSRTS_M_17_TS_00001', 
+  'audio': {
+    'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/9158ecbeeb3532038f3fe3d53e0adda1f790c9363a613bac32c454a39d9c682c/test/male/M_17/MSRTS_M_17_TS_00001.flac', 
+    'array': array([ 0.0020752 ,  0.00283813,  0.00167847, ..., -0.0010376 ,
+       -0.00091553, -0.00100708], dtype=float32), 
+    'sampling_rate': 16000
+  }, 
+  'speaker_id': 'M_17', 
+  'gender': 'male', 
+  'duration': 5.920000076293945, 
+  'normalized_text': 'ignazio saverio mifsud kien qed jippjana kien qed iħejji tliet volumi tal-biblijoteka maltese'
+}
+```
+### Data Fields
+* `audio_id` (string) - id of audio segment
+* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
+* `speaker_id` (string) - id of speaker
+* `gender` (string) - gender of speaker (male or female)
+* `duration` (float32) - duration of the audio file in seconds.
+* `normalized_text` (string) - normalized audio segment transcription
+### Data Splits
+The corpus counts just with the test split which has a total of 668 speech files from 17 male speakers and 17 female speakers with a total duration of 1 hour.
+## Dataset Creation
+### Curation Rationale
+The MASRI-TEST CORPUS (MTSC) has the following characteristics:
+* The MTSC has an exact duration of 1 hours and 0 minutes. It has 668 audio files.
+* The MTSC has recordings from 34 different speakers: 17 men and 17 women.
+* Data in MTSC is classified by speaker. Therefore, all the recordings of each individual speaker are stored in one single directory.
+* Data is also classified according to the gender (male/female) of the speakers.
+* Every audio file in the MTSC has a duration between 3 and 10 seconds approximately.
+* Audio files in the MTSC are distributed in a 16khz@16bit mono format.
+* Transcriptions in MTSC are in lowercase. No punctuation marks are permitted except for dashes (-) and apostrophes (') due to their importance in Maltese orthography.
+### Source Data
+#### Initial Data Collection and Normalization
+The MASRI-TEST CORPUS was possible due to a collaboration of two different Universities. The data selection and audio segmentation was performed by the [CIEMPIESS-UNAM Project](http://www.ciempiess.org/) at the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/) in Mexico City. The audio transcription and corpus edition was performed by the [MASRI Team](https://www.um.edu.mt/projects/masri/) at the [University of Malta](https://www.um.edu.mt/) in the Msida Campus.
+### Annotations
+#### Annotation process
+Proper nouns and other words pronounced in languages other than Maltese (mainly from English, Italian, French and German) were transcribed in their respective orthographic system.
+#### Who are the annotators?
+The audio transcription was performed by expert native speakers at the [University of Malta](https://www.um.edu.mt/) in the Msida Campus.
+### Personal and Sensitive Information
+The dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from a publicly repository (YouTube), so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.
+**Notice:** Should you consider that our data contains material that is owned by you and should therefore not be reproduced here?, please:
+* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
+* Clearly identify the copyrighted work claimed to be infringed.
+* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
+* Send the request to [Carlos Mena](mailto:carlos.mena@ciempiess.org)
+Take down: We will comply to legitimate requests by removing the affected sources from the corpus.
+## Considerations for Using the Data
+### Social Impact of Dataset
+This dataset is challenging because it contains spontaneous speech; so, it will be helpful for the ASR community to evaluate their acoustic models in Maltese with it.
+### Discussion of Biases
+The dataset intents to be gender balanced. It is comprised of 17 male speakers and 17 female speakers.
+### Other Known Limitations
+Neither the MASRI Team or the CIEMPIESS-UNAM Project guarantee the accuracy of this corpus, nor its suitability for any specific purpose. As a matter of fact, a number of errors, omissions and inconsistencies are expected to be found within the corpus.
+### Dataset Curators
+The audio recordings were collected and segmented by students belonging to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html), it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually performed by Ayrton-Didier Brincat during 2020.
+### Licensing Information
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). The copyright remains with the original owners of the video.
+As the data is taken from YouTube, we invoke the same argument of "fair use" as in the [Voxlingua107](http://bark.phon.ioc.ee/voxlingua107/) dataset, which is:
+**"While YouTube users own the copyright to their own videos, using the audio in the videos for training speech recognition models has very limited and transformative purpose and qualifies thus as "fair use" of copyrighted materials. YouTube’s terms of service forbid downloading, storing and distribution of videos. However, the aim of this rule is clearly to forbid unfair monetization of the content by third-party sites and applications. Our dataset contains the videos in segmented audio-only form that makes the monetization of the actual distributed content extremely difficult."**
+### Citation Information
+```
+@misc{carlosmenamasritest2020,
+      title={MASRI-TEST CORPUS: Audio and Transcriptions in Maltese extracted from the YouTube channel of the University of Malta.}, 
+      author={Hernandez Mena, Carlos Daniel and  Brincat, Ayrton-Didier and Gatt, Albert and DeMarco, Andrea and Borg, Claudia and van der Plas, Lonneke and Meza Ruiz, Iván Vladimir},
+      journal={MASRI Project, Malta},
+      year={2020},
+      url={https://huggingface.co/datasets/MLRS/masri_test},
+}
+```
+### Contributions
+The authors would like to thank to Alberto Templos Carbajal, Elena Vera and Angélica Gutiérrez for their support to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) at the ["Facultad de Ingeniería (FI)"](https://www.ingenieria.unam.mx/) of the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/). We also thank to the social service students for all the hard work during the audio segmentation.

huggingface_dataset/Dataset_Card/Nerfgun3_ouroboros_embeddings.md

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+---
+language:
+- en
+license: creativeml-openrail-m
+thumbnail: "https://huggingface.co/datasets/Nerfgun3/ouroboros_embeddings/resolve/main/ouroboros_showcase.jpg"
+tags:
+- stable-diffusion
+- text-to-image
+- image-to-image
+inference: false
+---
+
+# Ouroboros Style Embeddings / Textual Inversion
+
+<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/ouroboros_embeddings/resolve/main/ouroboros_showcase.jpg"/>
+
+## Intro
+
+Both embeddings are quiet similar in style, but were trained on a different dataset.
+
+## Usage
+
+To use my embeddings you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
+
+Personally, I would recommend to use my embeddings with a strength of 0.8, like ```"drawn by (filename:0.8)"```
+
+I trained both embeddings two epochs until 8000 steps.
+
+I hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: "Nerfgun3#7508"
+
+### Dark ouroboros
+
+This embedding was trained on a dataset with dark backgrounds.
+
+To use it in a prompt: ```"drawn by dark_ouroboros"```
+
+### White ouroboros
+
+This embedding was trained on a dataset with white backgrounds.
+
+To use it in a prompt: ```"drawn by white_ouroboros"```
+
+## License
+
+This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
+The CreativeML OpenRAIL License specifies: 
+
+1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 
+2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
+3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
+[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

huggingface_dataset/Dataset_Card/Nerfgun3_stripe_style.md

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+---
+language:
+- en
+license: creativeml-openrail-m
+thumbnail: "https://huggingface.co/datasets/Nerfgun3/stripe_style/resolve/main/stripe_style_showcase.jpg"
+tags:
+- stable-diffusion
+- text-to-image
+- image-to-image
+inference: false
+---
+
+# Stripe Style Embedding / Textual Inversion
+
+<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/stripe_style/resolve/main/stripe_style_showcase.jpg"/>
+
+## Usage
+
+To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
+
+To use it in a prompt: ```"drawn by stripe_style"```
+
+Personally, I would recommend to use my embeddings with a strength of 0.8, like ```"drawn by (stripe_style:0.8)"```
+
+I trained the embedding two epochs until 5000 steps.
+
+I hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: "Nerfgun3#7508"
+
+## License
+
+This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
+The CreativeML OpenRAIL License specifies: 
+
+1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 
+2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
+3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
+[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

huggingface_dataset/Dataset_Card/SetFit_ethos_binary.md

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+
+This is the binary split of [ethos](https://huggingface.co/datasets/ethos), split into train and test.
+
+It contains comments annotated for hate speech or not.

huggingface_dataset/Dataset_Card/allenai_qasper.md

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+---
+pretty_name: QASPER
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- en
+language_bcp47:
+- en-US
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|s2orc
+task_categories:
+- question-answering
+task_ids:
+- closed-domain-qa
+paperswithcode_id: qasper
+---
+
+# Dataset Card for Qasper
+
+## 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)
+      - [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://allenai.org/data/qasper](https://allenai.org/data/qasper)
+- **Demo:** [https://qasper-demo.apps.allenai.org/](https://qasper-demo.apps.allenai.org/)
+- **Paper:** [https://arxiv.org/abs/2105.03011](https://arxiv.org/abs/2105.03011)
+- **Blogpost:** [https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c](https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c)
+- **Leaderboards:** [https://paperswithcode.com/dataset/qasper](https://paperswithcode.com/dataset/qasper)
+
+### Dataset Summary
+
+QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers.
+
+### Supported Tasks and Leaderboards
+
+- `question-answering`: The dataset can be used to train a model for Question Answering. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 33.63 Token F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/question-answering-on-qasper)
+
+- `evidence-selection`: The dataset can be used to train a model for Evidence Selection. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 39.37 F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/evidence-selection-on-qasper)
+
+
+### Languages
+
+English, as it is used in research papers.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical instance in the dataset:
+
+```
+{
+  'id': "Paper ID (string)",
+  'title': "Paper Title",
+  'abstract': "paper abstract ...",
+  'full_text': {
+      'paragraphs':[["section1_paragraph1_text","section1_paragraph2_text",...],["section2_paragraph1_text","section2_paragraph2_text",...]],
+      'section_name':["section1_title","section2_title"],...},
+  'qas': {
+  'answers':[{
+      'annotation_id': ["q1_answer1_annotation_id","q1_answer2_annotation_id"]
+      'answer': [{
+          'unanswerable':False,
+          'extractive_spans':["q1_answer1_extractive_span1","q1_answer1_extractive_span2"],
+          'yes_no':False,
+          'free_form_answer':"q1_answer1",
+          'evidence':["q1_answer1_evidence1","q1_answer1_evidence2",..],
+          'highlighted_evidence':["q1_answer1_highlighted_evidence1","q1_answer1_highlighted_evidence2",..]
+          },
+          {
+          'unanswerable':False,
+          'extractive_spans':["q1_answer2_extractive_span1","q1_answer2_extractive_span2"],
+          'yes_no':False,
+          'free_form_answer':"q1_answer2",
+          'evidence':["q1_answer2_evidence1","q1_answer2_evidence2",..],
+          'highlighted_evidence':["q1_answer2_highlighted_evidence1","q1_answer2_highlighted_evidence2",..]
+          }],
+      'worker_id':["q1_answer1_worker_id","q1_answer2_worker_id"]
+      },{...["question2's answers"]..},{...["question3's answers"]..}],
+  'question':["question1","question2","question3"...],
+  'question_id':["question1_id","question2_id","question3_id"...],
+  'question_writer':["question1_writer_id","question2_writer_id","question3_writer_id"...],
+  'nlp_background':["question1_writer_nlp_background","question2_writer_nlp_background",...],
+  'topic_background':["question1_writer_topic_background","question2_writer_topic_background",...],
+  'paper_read': ["question1_writer_paper_read_status","question2_writer_paper_read_status",...],
+  'search_query':["question1_search_query","question2_search_query","question3_search_query"...],
+  }
+}
+```
+
+### Data Fields
+
+The following is an excerpt from the dataset README:
+
+Within "qas", some fields should be obvious. Here is some explanation about the others:
+
+#### Fields specific to questions:
+
+ - "nlp_background" shows the experience the question writer had. The values can be "zero" (no experience), "two" (0 - 2 years of experience), "five" (2 - 5 years of experience), and "infinity" (> 5 years of experience). The field may be empty as well, indicating the writer has chosen not to share this information.
+
+ - "topic_background" shows how familiar the question writer was with the topic of the paper. The values are "unfamiliar", "familiar", "research" (meaning that the topic is the research area of the writer), or null.
+
+ - "paper_read", when specified shows whether the questionwriter has read the paper.
+
+ - "search_query", if not empty, is the query the question writer used to find the abstract of the paper from a large pool of abstracts we made available to them.
+
+#### Fields specific to answers
+
+Unanswerable answers have "unanswerable" set to true. The remaining answers have exactly one of the following fields being non-empty.
+
+ - "extractive_spans" are spans in the paper which serve as the answer.
+ - "free_form_answer" is a written out answer.
+ - "yes_no" is true iff the answer is Yes, and false iff the answer is No.
+
+"evidence" is the set of paragraphs, figures or tables used to arrive at the answer. Tables or figures start with the string "FLOAT SELECTED"
+
+"highlighted_evidence" is the set of sentences the answer providers selected as evidence if they chose textual evidence. The text in the "evidence" field is a mapping from these sentences to the paragraph level. That is, if you see textual evidence in the "evidence" field, it is guaranteed to be entire paragraphs, while that is not the case with "highlighted_evidence".
+
+
+### Data Splits
+
+|                            | Train   | Valid |
+| -----                      | ------ | ----- |
+| Number of papers            |     888   |      281 |
+| Number of questions            |     2593   |      1005 |
+| Number of answers            |     2675   |      1764 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+NLP papers: The full text of the papers is extracted from [S2ORC](https://huggingface.co/datasets/s2orc) (Lo et al., 2020)
+
+#### 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?
+
+"The annotators are NLP practitioners, not
+expert researchers, and it is likely that an expert
+would score higher"
+
+### 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
+
+Crowdsourced NLP practitioners
+
+### Licensing Information
+
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0)
+
+### Citation Information
+
+```
+@inproceedings{Dasigi2021ADO,
+  title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
+  author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
+  year={2021}
+}
+```
+
+### Contributions
+
+Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en-78963b-2087067145.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/guess
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-7b1
+  metrics: []
+  dataset_name: futin/guess
+  dataset_config: en
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: bigscience/bloom-7b1
+* Dataset: futin/guess
+* Config: en
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-kmfoda__booksum-kmfoda__booksum-ee4836-2761681799.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- kmfoda/booksum
+eval_info:
+  task: summarization
+  model: pszemraj/tglobal-large-booksum-WIP3-K-r4
+  metrics: []
+  dataset_name: kmfoda/booksum
+  dataset_config: kmfoda--booksum
+  dataset_split: test
+  col_mapping:
+    text: chapter
+    target: summary_text
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: pszemraj/tglobal-large-booksum-WIP3-K-r4
+* Dataset: kmfoda/booksum
+* Config: kmfoda--booksum
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-596cbd-1668659070.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- mathemakitten/winobias_antistereotype_test
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-1.3b
+  metrics: ['f1', 'perplexity']
+  dataset_name: mathemakitten/winobias_antistereotype_test
+  dataset_config: mathemakitten--winobias_antistereotype_test
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: facebook/opt-1.3b
+* Dataset: mathemakitten/winobias_antistereotype_test
+* Config: mathemakitten--winobias_antistereotype_test
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@ddcas](https://huggingface.co/ddcas) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-samsum-61336320-1319050351.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- samsum
+eval_info:
+  task: summarization
+  model: facebook/bart-large-xsum
+  metrics: []
+  dataset_name: samsum
+  dataset_config: samsum
+  dataset_split: test
+  col_mapping:
+    text: dialogue
+    target: summary
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: facebook/bart-large-xsum
+* Dataset: samsum
+* Config: samsum
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@hgoyal194](https://huggingface.co/hgoyal194) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-autoevaluate__squad-sample-autoevaluate__squad-sample-778ba0-17436361.md

@@ -0,0 +1,35 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- autoevaluate/squad-sample
+eval_info:
+  task: extractive_question_answering
+  model: autoevaluate/roberta-base-squad2
+  metrics: []
+  dataset_name: autoevaluate/squad-sample
+  dataset_config: autoevaluate--squad-sample
+  dataset_split: test
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: autoevaluate/roberta-base-squad2
+* Dataset: autoevaluate/squad-sample
+* Config: autoevaluate--squad-sample
+* 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/imvladikon_bmc.md

@@ -0,0 +1,73 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- he
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|other-reuters-corpus
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+train-eval-index:
+- config: bmc
+  task: token-classification
+  task_id: entity_extraction
+  splits:
+    train_split: train
+    eval_split: validation
+    test_split: test
+  col_mapping:
+    tokens: tokens
+    ner_tags: tags
+  metrics:
+    - type: seqeval
+      name: seqeval
+---
+
+
+# Splits for the Ben-Mordecai and Elhadad Hebrew NER Corpus (BMC)
+
+In order to evaluate performance in accordance with the original Ben-Mordecai and Elhadad (2005) work, we provide three 75%-25% random splits. 
+* Only the 7 entity categories viable for evaluation were kept (DATE, LOC, MONEY, ORG, PER, PERCENT, TIME) --- all MISC entities were filtered out.
+* Sequence label scheme was changed from IOB to BIOES
+* The dev sets are 10% taken out of the 75%
+
+
+## Citation
+
+If you use use the BMC corpus, please cite the original paper as well as our paper which describes the splits:
+
+* Ben-Mordecai and Elhadad (2005):
+```console
+@mastersthesis{naama,
+  title={Hebrew Named Entity Recognition},
+  author={Ben-Mordecai, Naama},
+  advisor={Elhadad, Michael},
+  year={2005},
+  url="https://www.cs.bgu.ac.il/~elhadad/nlpproj/naama/",
+  institution={Department of Computer Science, Ben-Gurion University},
+  school={Department of Computer Science, Ben-Gurion University},
+}
+```
+
+* Bareket and Tsarfaty (2020)
+```console
+@misc{bareket2020neural,
+      title={Neural Modeling for Named Entities and Morphology (NEMO^2)}, 
+      author={Dan Bareket and Reut Tsarfaty},
+      year={2020},
+      eprint={2007.15620},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+

huggingface_dataset/Dataset_Card/mc4.md

@@ -0,0 +1,529 @@
+---
+pretty_name: mC4
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- af
+- am
+- ar
+- az
+- be
+- bg
+- bn
+- ca
+- ceb
+- co
+- cs
+- cy
+- da
+- de
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fil
+- fr
+- fy
+- ga
+- gd
+- gl
+- gu
+- ha
+- haw
+- he
+- hi
+- hmn
+- ht
+- hu
+- hy
+- id
+- ig
+- is
+- it
+- iw
+- ja
+- jv
+- ka
+- kk
+- km
+- kn
+- ko
+- ku
+- ky
+- la
+- lb
+- lo
+- lt
+- lv
+- mg
+- mi
+- mk
+- ml
+- mn
+- mr
+- ms
+- mt
+- my
+- ne
+- nl
+- 'no'
+- ny
+- pa
+- pl
+- ps
+- pt
+- ro
+- ru
+- sd
+- si
+- sk
+- sl
+- sm
+- sn
+- so
+- sq
+- sr
+- st
+- su
+- sv
+- sw
+- ta
+- te
+- tg
+- th
+- tr
+- uk
+- und
+- ur
+- uz
+- vi
+- xh
+- yi
+- yo
+- zh
+- zu
+language_bcp47:
+- bg-Latn
+- el-Latn
+- hi-Latn
+- ja-Latn
+- ru-Latn
+- zh-Latn
+license:
+- odc-by
+multilinguality:
+- multilingual
+size_categories:
+- n<1K
+- 1K<n<10K
+- 10K<n<100K
+- 100K<n<1M
+- 1M<n<10M
+- 10M<n<100M
+- 100M<n<1B
+- 1B<n<10B
+source_datasets:
+- original
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- language-modeling
+- masked-language-modeling
+paperswithcode_id: mc4
+---
+
+# Dataset Card for mC4
+
+## Table of Contents
+
+- [Dataset Card for mC4](#dataset-card-for-mc4)
+  - [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)
+      - [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://huggingface.co/datasets/allenai/c4
+- **Paper:** https://arxiv.org/abs/1910.10683
+
+### Dataset Summary
+
+A multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org".
+
+This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4
+
+108 languages are available and are reported in the table below.
+
+Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script.
+
+| language code   | language name        |
+|:----------------|:---------------------|
+| af              | Afrikaans            |
+| am              | Amharic              |
+| ar              | Arabic               |
+| az              | Azerbaijani          |
+| be              | Belarusian           |
+| bg              | Bulgarian            |
+| bg-Latn         | Bulgarian (Latin)    |
+| bn              | Bangla               |
+| ca              | Catalan              |
+| ceb             | Cebuano              |
+| co              | Corsican             |
+| cs              | Czech                |
+| cy              | Welsh                |
+| da              | Danish               |
+| de              | German               |
+| el              | Greek                |
+| el-Latn         | Greek (Latin)        |
+| en              | English              |
+| eo              | Esperanto            |
+| es              | Spanish              |
+| et              | Estonian             |
+| eu              | Basque               |
+| fa              | Persian              |
+| fi              | Finnish              |
+| fil             | Filipino             |
+| fr              | French               |
+| fy              | Western Frisian      |
+| ga              | Irish                |
+| gd              | Scottish Gaelic      |
+| gl              | Galician             |
+| gu              | Gujarati             |
+| ha              | Hausa                |
+| haw             | Hawaiian             |
+| hi              | Hindi                |
+| hi-Latn         | Hindi (Latin script) |
+| hmn             | Hmong, Mong          |
+| ht              | Haitian              |
+| hu              | Hungarian            |
+| hy              | Armenian             |
+| id              | Indonesian           |
+| ig              | Igbo                 |
+| is              | Icelandic            |
+| it              | Italian              |
+| iw              | former Hebrew        |
+| ja              | Japanese             |
+| ja-Latn         | Japanese (Latin)     |
+| jv              | Javanese             |
+| ka              | Georgian             |
+| kk              | Kazakh               |
+| km              | Khmer                |
+| kn              | Kannada              |
+| ko              | Korean               |
+| ku              | Kurdish              |
+| ky              | Kyrgyz               |
+| la              | Latin                |
+| lb              | Luxembourgish        |
+| lo              | Lao                  |
+| lt              | Lithuanian           |
+| lv              | Latvian              |
+| mg              | Malagasy             |
+| mi              | Maori                |
+| mk              | Macedonian           |
+| ml              | Malayalam            |
+| mn              | Mongolian            |
+| mr              | Marathi              |
+| ms              | Malay                |
+| mt              | Maltese              |
+| my              | Burmese              |
+| ne              | Nepali               |
+| nl              | Dutch                |
+| no              | Norwegian            |
+| ny              | Nyanja               |
+| pa              | Punjabi              |
+| pl              | Polish               |
+| ps              | Pashto               |
+| pt              | Portuguese           |
+| ro              | Romanian             |
+| ru              | Russian              |
+| ru-Latn         | Russian (Latin)      |
+| sd              | Sindhi               |
+| si              | Sinhala              |
+| sk              | Slovak               |
+| sl              | Slovenian            |
+| sm              | Samoan           |
+| sn              | Shona                |
+| so              | Somali               |
+| sq              | Albanian             |
+| sr              | Serbian              |
+| st              | Southern Sotho       |
+| su              | Sundanese            |
+| sv              | Swedish              |
+| sw              | Swahili              |
+| ta              | Tamil                |
+| te              | Telugu               |
+| tg              | Tajik                |
+| th              | Thai                 |
+| tr              | Turkish              |
+| uk              | Ukrainian            |
+| und             | Unknown language     |
+| ur              | Urdu                 |
+| uz              | Uzbek                |
+| vi              | Vietnamese           |
+| xh              | Xhosa                |
+| yi              | Yiddish              |
+| yo              | Yoruba               |
+| zh              | Chinese              |
+| zh-Latn         | Chinese (Latin)      |
+| zu              | Zulu                 |
+
+You can load the mC4 subset of any language like this:
+
+```python
+from datasets import load_dataset
+
+en_mc4 = load_dataset("mc4", "en")
+```
+
+And if you can even specify a list of languages:
+
+```python
+from datasets import load_dataset
+
+mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"])
+```
+
+### Supported Tasks and Leaderboards
+
+mC4 is mainly intended to pretrain language models and word representations.
+
+### Languages
+
+The dataset supports 108 languages.
+
+## Dataset Structure
+
+### Data Instances
+
+An example form the `en` config is:
+
+```
+{'timestamp': '2018-06-24T01:32:39Z',
+ 'text': 'Farm Resources in Plumas County\nShow Beginning Farmer Organizations & Professionals (304)\nThere are 304 resources serving Plumas County in the following categories:\nMap of Beginning Farmer Organizations & Professionals serving Plumas County\nVictoria Fisher - Office Manager - Loyalton, CA\nAmy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\nShow Farm Income Opportunities Organizations & Professionals (353)\nThere are 353 resources serving Plumas County in the following categories:\nFarm Ranch And Forest Retailers (18)\nMap of Farm Income Opportunities Organizations & Professionals serving Plumas County\nWarner Valley Wildlife Area - Plumas County\nShow Farm Resources Organizations & Professionals (297)\nThere are 297 resources serving Plumas County in the following categories:\nMap of Farm Resources Organizations & Professionals serving Plumas County\nThere are 57 resources serving Plumas County in the following categories:\nMap of Organic Certification Organizations & Professionals serving Plumas County',
+ 'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'}
+```
+
+### Data Fields
+
+The data have several fields:
+
+- `url`: url of the source as a string
+- `text`: text content as a string
+- `timestamp`: timestamp as a string
+
+### Data Splits
+
+To build mC4, the authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table:
+
+| config   | train   | validation   |
+|:---------|:--------|:-------------|
+| af       | ?       | ?            |
+| am       | ?       | ?            |
+| ar       | ?       | ?            |
+| az       | ?       | ?            |
+| be       | ?       | ?            |
+| bg       | ?       | ?            |
+| bg-Latn  | ?       | ?            |
+| bn       | ?       | ?            |
+| ca       | ?       | ?            |
+| ceb      | ?       | ?            |
+| co       | ?       | ?            |
+| cs       | ?       | ?            |
+| cy       | ?       | ?            |
+| da       | ?       | ?            |
+| de       | ?       | ?            |
+| el       | ?       | ?            |
+| el-Latn  | ?       | ?            |
+| en       | ?       | ?            |
+| eo       | ?       | ?            |
+| es       | ?       | ?            |
+| et       | ?       | ?            |
+| eu       | ?       | ?            |
+| fa       | ?       | ?            |
+| fi       | ?       | ?            |
+| fil      | ?       | ?            |
+| fr       | ?       | ?            |
+| fy       | ?       | ?            |
+| ga       | ?       | ?            |
+| gd       | ?       | ?            |
+| gl       | ?       | ?            |
+| gu       | ?       | ?            |
+| ha       | ?       | ?            |
+| haw      | ?       | ?            |
+| hi       | ?       | ?            |
+| hi-Latn  | ?       | ?            |
+| hmn      | ?       | ?            |
+| ht       | ?       | ?            |
+| hu       | ?       | ?            |
+| hy       | ?       | ?            |
+| id       | ?       | ?            |
+| ig       | ?       | ?            |
+| is       | ?       | ?            |
+| it       | ?       | ?            |
+| iw       | ?       | ?            |
+| ja       | ?       | ?            |
+| ja-Latn  | ?       | ?            |
+| jv       | ?       | ?            |
+| ka       | ?       | ?            |
+| kk       | ?       | ?            |
+| km       | ?       | ?            |
+| kn       | ?       | ?            |
+| ko       | ?       | ?            |
+| ku       | ?       | ?            |
+| ky       | ?       | ?            |
+| la       | ?       | ?            |
+| lb       | ?       | ?            |
+| lo       | ?       | ?            |
+| lt       | ?       | ?            |
+| lv       | ?       | ?            |
+| mg       | ?       | ?            |
+| mi       | ?       | ?            |
+| mk       | ?       | ?            |
+| ml       | ?       | ?            |
+| mn       | ?       | ?            |
+| mr       | ?       | ?            |
+| ms       | ?       | ?            |
+| mt       | ?       | ?            |
+| my       | ?       | ?            |
+| ne       | ?       | ?            |
+| nl       | ?       | ?            |
+| no       | ?       | ?            |
+| ny       | ?       | ?            |
+| pa       | ?       | ?            |
+| pl       | ?       | ?            |
+| ps       | ?       | ?            |
+| pt       | ?       | ?            |
+| ro       | ?       | ?            |
+| ru       | ?       | ?            |
+| ru-Latn  | ?       | ?            |
+| sd       | ?       | ?            |
+| si       | ?       | ?            |
+| sk       | ?       | ?            |
+| sl       | ?       | ?            |
+| sm       | ?       | ?            |
+| sn       | ?       | ?            |
+| so       | ?       | ?            |
+| sq       | ?       | ?            |
+| sr       | ?       | ?            |
+| st       | ?       | ?            |
+| su       | ?       | ?            |
+| sv       | ?       | ?            |
+| sw       | ?       | ?            |
+| ta       | ?       | ?            |
+| te       | ?       | ?            |
+| tg       | ?       | ?            |
+| th       | ?       | ?            |
+| tr       | ?       | ?            |
+| uk       | ?       | ?            |
+| und      | ?       | ?            |
+| ur       | ?       | ?            |
+| uz       | ?       | ?            |
+| vi       | ?       | ?            |
+| xh       | ?       | ?            |
+| yi       | ?       | ?            |
+| yo       | ?       | ?            |
+| zh       | ?       | ?            |
+| zh-Latn  | ?       | ?            |
+| zu       | ?       | ?            |
+
+## 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
+
+AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
+
+### Citation Information
+
+```
+@article{2019t5,
+    author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
+    title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
+    journal = {arXiv e-prints},
+    year = {2019},
+    archivePrefix = {arXiv},
+    eprint = {1910.10683},
+}
+```
+
+### Contributions
+
+Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

huggingface_dataset/Dataset_Card/saibo_bookcorpus_small_compact_1024_n7.md

@@ -0,0 +1,21 @@
+---
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: concept_with_offset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 81072
+    num_examples: 7
+  download_size: 42603
+  dataset_size: 81072
+---
+# Dataset Card for "bookcorpus_small_compact_1024_n7"
+
+448 samples after explode graphs
+
+`gdown 13QYq8op5XHlhL_qvdQbpYxo-pR5uAwcO` to download the assciated graph pickle
+
+[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

huggingface_dataset/Dataset_Card/taln-ls2n_kp20k.md

@@ -0,0 +1,61 @@
+---
+annotations_creators:
+- unknown
+language_creators:
+- unknown
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+task_ids: []
+pretty_name: KP20k
+tags:
+- keyphrase-generation
+- keyphrase-extraction
+- text-mining
+---
+
+# KP20k Benchmark Dataset for Keyphrase Generation
+
+## About
+
+KP20k is a dataset for benchmarking keyphrase extraction and generation models.
+The data is composed of 570 809 abstracts and their associated titles from scientific articles.
+
+Details about the dataset can be found in the original paper:
+- Meng et al 2017.
+  [Deep keyphrase Generation](https://aclanthology.org/P17-1054.pdf)
+  Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 582–592
+  
+Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
+ - Florian Boudin and Ygor Gallina. 2021.
+   [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). 
+   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
+   
+Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.   
+  
+## Content
+
+The dataset is divided into the following three splits:
+
+| Split      | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
+| :--------- | ----------: | -----------:                       | --------: | ----------: | ------: | -------: |
+| Train      | 530 809     |                5.29                |   58.19   | 10.93       | 17.36   |   13.52  |
+| Test       | 20 000      |                5.28                |   58.40   | 10.84       | 17.20   |   13.56  |
+| Validation | 20 000      |                5.27                |   58.20   | 10.94       | 17.26   |   13.61  |
+
+
+The following data fields are available:
+- **id**: unique identifier of the document. **NB** There were no ids in the original dataset. The ids were generated using the python module shortuuid (https://pypi.org/project/shortuuid/)
+- **title**: title of the document.
+- **abstract**: abstract of the document.
+- **keyphrases**: list of reference keyphrases.
+- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
+
+**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + abstract).

huggingface_dataset/Dataset_Card/tner_tweebank_ner.md

@@ -0,0 +1,86 @@
+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 1k<10K
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: TweeBank NER
+---
+
+# Dataset Card for "tner/tweebank_ner"
+
+## Dataset Description
+
+- **Repository:** [T-NER](https://github.com/asahi417/tner)
+- **Paper:** [https://arxiv.org/abs/2201.07281](https://arxiv.org/abs/2201.07281)
+- **Dataset:** TweeBank NER
+- **Domain:** Twitter
+- **Number of Entity:** 4
+
+
+### Dataset Summary
+TweeBank NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
+- Entity Types: `LOC`, `MISC`, `PER`, `ORG`
+
+## Dataset Structure
+
+### Data Instances
+An example of `train` looks as follows.
+
+```
+{
+    'tokens': ['RT', '@USER2362', ':', 'Farmall', 'Heart', 'Of', 'The', 'Holidays', 'Tabletop', 'Christmas', 'Tree', 'With', 'Lights', 'And', 'Motion', 'URL1087', '#Holiday', '#Gifts'],
+    'tags': [8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]
+}
+```
+
+### Label ID
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweebank_ner/raw/main/dataset/label.json).
+```python
+{
+    "B-LOC": 0,
+    "B-MISC": 1,
+    "B-ORG": 2,
+    "B-PER": 3,
+    "I-LOC": 4,
+    "I-MISC": 5,
+    "I-ORG": 6,
+    "I-PER": 7,
+    "O": 8
+}
+```
+
+### Data Splits
+
+|  name   |train|validation|test|
+|---------|----:|---------:|---:|
+|tweebank_ner | 1639|      710 |1201|
+
+### Citation Information
+
+```
+@article{DBLP:journals/corr/abs-2201-07281,
+  author    = {Hang Jiang and
+               Yining Hua and
+               Doug Beeferman and
+               Deb Roy},
+  title     = {Annotating the Tweebank Corpus on Named Entity Recognition and Building
+               {NLP} Models for Social Media Analysis},
+  journal   = {CoRR},
+  volume    = {abs/2201.07281},
+  year      = {2022},
+  url       = {https://arxiv.org/abs/2201.07281},
+  eprinttype = {arXiv},
+  eprint    = {2201.07281},
+  timestamp = {Fri, 21 Jan 2022 13:57:15 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2201-07281.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

huggingface_dataset/Dataset_Card/tner_wikiann.md

@@ -0,0 +1,423 @@
+---
+language:
+- ace
+- bg
+- da
+- fur
+- ilo
+- lij
+- mzn
+- qu
+- su
+- vi
+- af
+- bh
+- de
+- fy
+- io
+- lmo
+- nap
+- rm
+- sv
+- vls
+- als
+- bn
+- diq
+- ga
+- is
+- ln
+- nds
+- ro
+- sw
+- vo
+- am
+- bo
+- dv
+- gan
+- it
+- lt
+- ne
+- ru
+- szl
+- wa
+- an
+- br
+- el
+- gd
+- ja
+- lv
+- nl
+- rw
+- ta
+- war
+- ang
+- bs
+- eml
+- gl
+- jbo
+- nn
+- sa
+- te
+- wuu
+- ar
+- ca
+- en
+- gn
+- jv
+- mg
+- no
+- sah
+- tg
+- xmf
+- arc
+- eo
+- gu
+- ka
+- mhr
+- nov
+- scn
+- th
+- yi
+- arz
+- cdo
+- es
+- hak
+- kk
+- mi
+- oc
+- sco
+- tk
+- yo
+- as
+- ce
+- et
+- he
+- km
+- min
+- or
+- sd
+- tl
+- zea
+- ast
+- ceb
+- eu
+- hi
+- kn
+- mk
+- os
+- sh
+- tr
+- ay
+- ckb
+- ext
+- hr
+- ko
+- ml
+- pa
+- si
+- tt
+- az
+- co
+- fa
+- hsb
+- ksh
+- mn
+- pdc
+- ug
+- ba
+- crh
+- fi
+- hu
+- ku
+- mr
+- pl
+- sk
+- uk
+- zh
+- bar
+- cs
+- hy
+- ky
+- ms
+- pms
+- sl
+- ur
+- csb
+- fo
+- ia
+- la
+- mt
+- pnb
+- so
+- uz
+- cv
+- fr
+- id
+- lb
+- mwl
+- ps
+- sq
+- vec
+- be
+- cy
+- frr
+- ig
+- li
+- my
+- pt
+- sr
+multilinguality:
+- multilingual
+size_categories:
+- 10K<100k
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: WikiAnn
+---
+
+# Dataset Card for "tner/wikiann"
+
+## Dataset Description
+
+- **Repository:** [T-NER](https://github.com/asahi417/tner)
+- **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/)
+- **Dataset:** WikiAnn
+- **Domain:** Wikipedia
+- **Number of Entity:** 3
+
+
+### Dataset Summary
+WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
+- Entity Types: `LOC`, `ORG`, `PER`
+
+## Dataset Structure
+
+### Data Instances
+An example of `train` of `ja` looks as follows.
+
+```
+{
+  'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'],
+  'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6]
+}
+```
+
+### Label ID
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json).
+```python
+{
+    "B-LOC": 0,
+    "B-ORG": 1,
+    "B-PER": 2,
+    "I-LOC": 3,
+    "I-ORG": 4,
+    "I-PER": 5,
+    "O": 6
+}
+```
+
+### Data Splits
+
+| language     |   train |   validation |   test |
+|:-------------|--------:|-------------:|-------:|
+| ace          |     100 |          100 |    100 |
+| bg           |   20000 |        10000 |  10000 |
+| da           |   20000 |        10000 |  10000 |
+| fur          |     100 |          100 |    100 |
+| ilo          |     100 |          100 |    100 |
+| lij          |     100 |          100 |    100 |
+| mzn          |     100 |          100 |    100 |
+| qu           |     100 |          100 |    100 |
+| su           |     100 |          100 |    100 |
+| vi           |   20000 |        10000 |  10000 |
+| af           |    5000 |         1000 |   1000 |
+| bh           |     100 |          100 |    100 |
+| de           |   20000 |        10000 |  10000 |
+| fy           |    1000 |         1000 |   1000 |
+| io           |     100 |          100 |    100 |
+| lmo          |     100 |          100 |    100 |
+| nap          |     100 |          100 |    100 |
+| rm           |     100 |          100 |    100 |
+| sv           |   20000 |        10000 |  10000 |
+| vls          |     100 |          100 |    100 |
+| als          |     100 |          100 |    100 |
+| bn           |   10000 |         1000 |   1000 |
+| diq          |     100 |          100 |    100 |
+| ga           |    1000 |         1000 |   1000 |
+| is           |    1000 |         1000 |   1000 |
+| ln           |     100 |          100 |    100 |
+| nds          |     100 |          100 |    100 |
+| ro           |   20000 |        10000 |  10000 |
+| sw           |    1000 |         1000 |   1000 |
+| vo           |     100 |          100 |    100 |
+| am           |     100 |          100 |    100 |
+| bo           |     100 |          100 |    100 |
+| dv           |     100 |          100 |    100 |
+| gan          |     100 |          100 |    100 |
+| it           |   20000 |        10000 |  10000 |
+| lt           |   10000 |        10000 |  10000 |
+| ne           |     100 |          100 |    100 |
+| ru           |   20000 |        10000 |  10000 |
+| szl          |     100 |          100 |    100 |
+| wa           |     100 |          100 |    100 |
+| an           |    1000 |         1000 |   1000 |
+| br           |    1000 |         1000 |   1000 |
+| el           |   20000 |        10000 |  10000 |
+| gd           |     100 |          100 |    100 |
+| ja           |   20000 |        10000 |  10000 |
+| lv           |   10000 |        10000 |  10000 |
+| nl           |   20000 |        10000 |  10000 |
+| rw           |     100 |          100 |    100 |
+| ta           |   15000 |         1000 |   1000 |
+| war          |     100 |          100 |    100 |
+| ang          |     100 |          100 |    100 |
+| bs           |   15000 |         1000 |   1000 |
+| eml          |     100 |          100 |    100 |
+| gl           |   15000 |        10000 |  10000 |
+| jbo          |     100 |          100 |    100 |
+| map-bms      |     100 |          100 |    100 |
+| nn           |   20000 |         1000 |   1000 |
+| sa           |     100 |          100 |    100 |
+| te           |    1000 |         1000 |   1000 |
+| wuu          |     100 |          100 |    100 |
+| ar           |   20000 |        10000 |  10000 |
+| ca           |   20000 |        10000 |  10000 |
+| en           |   20000 |        10000 |  10000 |
+| gn           |     100 |          100 |    100 |
+| jv           |     100 |          100 |    100 |
+| mg           |     100 |          100 |    100 |
+| no           |   20000 |        10000 |  10000 |
+| sah          |     100 |          100 |    100 |
+| tg           |     100 |          100 |    100 |
+| xmf          |     100 |          100 |    100 |
+| arc          |     100 |          100 |    100 |
+| cbk-zam      |     100 |          100 |    100 |
+| eo           |   15000 |        10000 |  10000 |
+| gu           |     100 |          100 |    100 |
+| ka           |   10000 |        10000 |  10000 |
+| mhr          |     100 |          100 |    100 |
+| nov          |     100 |          100 |    100 |
+| scn          |     100 |          100 |    100 |
+| th           |   20000 |        10000 |  10000 |
+| yi           |     100 |          100 |    100 |
+| arz          |     100 |          100 |    100 |
+| cdo          |     100 |          100 |    100 |
+| es           |   20000 |        10000 |  10000 |
+| hak          |     100 |          100 |    100 |
+| kk           |    1000 |         1000 |   1000 |
+| mi           |     100 |          100 |    100 |
+| oc           |     100 |          100 |    100 |
+| sco          |     100 |          100 |    100 |
+| tk           |     100 |          100 |    100 |
+| yo           |     100 |          100 |    100 |
+| as           |     100 |          100 |    100 |
+| ce           |     100 |          100 |    100 |
+| et           |   15000 |        10000 |  10000 |
+| he           |   20000 |        10000 |  10000 |
+| km           |     100 |          100 |    100 |
+| min          |     100 |          100 |    100 |
+| or           |     100 |          100 |    100 |
+| sd           |     100 |          100 |    100 |
+| tl           |   10000 |         1000 |   1000 |
+| zea          |     100 |          100 |    100 |
+| ast          |    1000 |         1000 |   1000 |
+| ceb          |     100 |          100 |    100 |
+| eu           |   10000 |        10000 |  10000 |
+| hi           |    5000 |         1000 |   1000 |
+| kn           |     100 |          100 |    100 |
+| mk           |   10000 |         1000 |   1000 |
+| os           |     100 |          100 |    100 |
+| sh           |   20000 |        10000 |  10000 |
+| tr           |   20000 |        10000 |  10000 |
+| zh-classical |     100 |          100 |    100 |
+| ay           |     100 |          100 |    100 |
+| ckb          |    1000 |         1000 |   1000 |
+| ext          |     100 |          100 |    100 |
+| hr           |   20000 |        10000 |  10000 |
+| ko           |   20000 |        10000 |  10000 |
+| ml           |   10000 |         1000 |   1000 |
+| pa           |     100 |          100 |    100 |
+| si           |     100 |          100 |    100 |
+| tt           |    1000 |         1000 |   1000 |
+| zh-min-nan   |     100 |          100 |    100 |
+| az           |   10000 |         1000 |   1000 |
+| co           |     100 |          100 |    100 |
+| fa           |   20000 |        10000 |  10000 |
+| hsb          |     100 |          100 |    100 |
+| ksh          |     100 |          100 |    100 |
+| mn           |     100 |          100 |    100 |
+| pdc          |     100 |          100 |    100 |
+| simple       |   20000 |         1000 |   1000 |
+| ug           |     100 |          100 |    100 |
+| zh-yue       |   20000 |        10000 |  10000 |
+| ba           |     100 |          100 |    100 |
+| crh          |     100 |          100 |    100 |
+| fi           |   20000 |        10000 |  10000 |
+| hu           |   20000 |        10000 |  10000 |
+| ku           |     100 |          100 |    100 |
+| mr           |    5000 |         1000 |   1000 |
+| pl           |   20000 |        10000 |  10000 |
+| sk           |   20000 |        10000 |  10000 |
+| uk           |   20000 |        10000 |  10000 |
+| zh           |   20000 |        10000 |  10000 |
+| bar          |     100 |          100 |    100 |
+| cs           |   20000 |        10000 |  10000 |
+| fiu-vro      |     100 |          100 |    100 |
+| hy           |   15000 |         1000 |   1000 |
+| ky           |     100 |          100 |    100 |
+| ms           |   20000 |         1000 |   1000 |
+| pms          |     100 |          100 |    100 |
+| sl           |   15000 |        10000 |  10000 |
+| ur           |   20000 |         1000 |   1000 |
+| bat-smg      |     100 |          100 |    100 |
+| csb          |     100 |          100 |    100 |
+| fo           |     100 |          100 |    100 |
+| ia           |     100 |          100 |    100 |
+| la           |    5000 |         1000 |   1000 |
+| mt           |     100 |          100 |    100 |
+| pnb          |     100 |          100 |    100 |
+| so           |     100 |          100 |    100 |
+| uz           |    1000 |         1000 |   1000 |
+| be-x-old     |    5000 |         1000 |   1000 |
+| cv           |     100 |          100 |    100 |
+| fr           |   20000 |        10000 |  10000 |
+| id           |   20000 |        10000 |  10000 |
+| lb           |    5000 |         1000 |   1000 |
+| mwl          |     100 |          100 |    100 |
+| ps           |     100 |          100 |    100 |
+| sq           |    5000 |         1000 |   1000 |
+| vec          |     100 |          100 |    100 |
+| be           |   15000 |         1000 |   1000 |
+| cy           |   10000 |         1000 |   1000 |
+| frr          |     100 |          100 |    100 |
+| ig           |     100 |          100 |    100 |
+| li           |     100 |          100 |    100 |
+| my           |     100 |          100 |    100 |
+| pt           |   20000 |        10000 |  10000 |
+| sr           |   20000 |        10000 |  10000 |
+| vep          |     100 |          100 |    100 |
+
+### Citation Information
+
+```
+@inproceedings{pan-etal-2017-cross,
+    title = "Cross-lingual Name Tagging and Linking for 282 Languages",
+    author = "Pan, Xiaoman  and
+      Zhang, Boliang  and
+      May, Jonathan  and
+      Nothman, Joel  and
+      Knight, Kevin  and
+      Ji, Heng",
+    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://aclanthology.org/P17-1178",
+    doi = "10.18653/v1/P17-1178",
+    pages = "1946--1958",
+    abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
+}
+```

huggingface_dataset/Dataset_Card/unza_unza-nyanja.md

@@ -0,0 +1 @@
+This dataset is for use in Automatic Speech Recognition (ASR) for a project at University of Zambia(UNZA)

huggingface_dataset/Dataset_Card/wikimedia_wit_base.md

@@ -0,0 +1,470 @@
+---
+annotations_creators:
+- machine-generated
+language_creators:
+- found
+language:
+- af
+- an
+- ar
+- arz
+- ast
+- az
+- azb
+- ba
+- bar
+- be
+- bg
+- bn
+- br
+- bs
+- ca
+- ce
+- ceb
+- ckb
+- cs
+- cv
+- cy
+- da
+- de
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fil
+- fr
+- fy
+- ga
+- gl
+- hi
+- hr
+- hsb
+- ht
+- hu
+- hy
+- ia
+- id
+- io
+- is
+- it
+- iw
+- ja
+- jv
+- ka
+- kk
+- kn
+- ko
+- la
+- lah
+- lb
+- lmo
+- lt
+- lv
+- mg
+- mk
+- ml
+- mn
+- mr
+- ms
+- my
+- nan
+- nds
+- ne
+- nl
+- nn
+- 'no'
+- nv
+- oc
+- pa
+- pl
+- pt
+- qu
+- ro
+- ru
+- sco
+- si
+- sk
+- sl
+- sq
+- sr
+- sv
+- sw
+- ta
+- te
+- tg
+- th
+- tr
+- tt
+- uk
+- ur
+- uz
+- vec
+- vi
+- vo
+- war
+- xmf
+- yue
+- zh
+license:
+- cc-by-sa-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+- extended|wikipedia
+task_categories:
+- image-to-text
+- text-retrieval
+task_ids:
+- image-captioning
+paperswithcode_id: wit
+pretty_name: Wikipedia-based Image Text
+language_bcp47:
+- af
+- an
+- ar
+- arz
+- ast
+- az
+- azb
+- ba
+- bar
+- be
+- be-tarask
+- bg
+- bn
+- br
+- bs
+- ca
+- ce
+- ceb
+- ckb
+- cs
+- cv
+- cy
+- da
+- de
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fil
+- fr
+- fy
+- ga
+- gl
+- hi
+- hr
+- hsb
+- ht
+- hu
+- hy
+- ia
+- id
+- io
+- is
+- it
+- iw
+- ja
+- jv
+- ka
+- kk
+- kn
+- ko
+- la
+- lah
+- lb
+- lmo
+- lt
+- lv
+- mg
+- mk
+- ml
+- mn
+- mr
+- ms
+- my
+- nan
+- nds
+- ne
+- nl
+- nn
+- 'no'
+- nv
+- oc
+- pa
+- pl
+- pt
+- qu
+- ro
+- ru
+- sco
+- si
+- sk
+- sl
+- sq
+- sr
+- sr-Latn
+- sv
+- sw
+- ta
+- te
+- tg
+- th
+- tr
+- tt
+- uk
+- ur
+- uz
+- vec
+- vi
+- vo
+- war
+- xmf
+- yue
+- zh
+- zh-TW
+tags:
+- text-image-retrieval
+---
+
+# Dataset Card for WIT
+
+## 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:** [WIT homepage](https://github.com/google-research-datasets/wit)
+- **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
+](https://arxiv.org/abs/2103.01913)
+- **Leaderboard:** [WIT leaderboard](https://paperswithcode.com/sota/text-image-retrieval-on-wit) and [WIT Kaggle competition](https://www.kaggle.com/competitions/wikipedia-image-caption/leaderboard)
+- **Point of Contact:** [Miriam Redi](mailto:miriam@wikimedia.org)
+
+### Dataset Summary
+
+Wikimedia's version of the Wikipedia-based Image Text (WIT) Dataset, a large multimodal multilingual dataset.
+
+From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/):
+
+> The core training data is taken from the Wikipedia Image-Text (WIT) Dataset, a large curated set of more than 37 million image-text associations extracted from Wikipedia articles in 108 languages that was recently released by Google Research.
+>
+> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images. However, due to licensing and data volume issues, the Google dataset only provides the image name and corresponding URL for download and not the raw image files.
+>
+> Getting easy access to the image files is crucial for participants to successfully develop competitive models. Therefore, today, the Wikimedia Research team is releasing its first large image dataset. It contains more than six million image files from Wikipedia articles in 100+ languages, which correspond to almost [1] all captioned images in the WIT dataset. Image files are provided at a 300-px resolution, a size that is suitable for most of the learning frameworks used to classify and analyze images.
+
+> [1] We are publishing all images having a non-null “reference description” in the WIT dataset.  For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the RetinaFace detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are candidate for deletion on Commons from the dataset.
+
+**Note**: Compared to [Google's version](https://huggingface.co/datasets/google/wit), which has contents of one Wikipedia page per data sample, this version groups contents of all Wikipedia pages available in different languages for the image in one single data sample to avoid duplication of image bytes.
+
+### Supported Tasks and Leaderboards
+
+- `image-captioning`: This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.
+
+- `text-retrieval`: The goal in this task is to build a model that retrieves the text (`caption_title_and_reference_description`) closest to an image. The leaderboard for this task can be found [here](https://paperswithcode.com/sota/text-image-retrieval-on-wit). This task also has a competition on [Kaggle](https://www.kaggle.com/c/wikipedia-image-caption). 
+
+In these tasks, any combination of the `caption_reference_description`, `caption_attribution_description` and `caption_alt_text_description` fields can be used as the input text/caption. 
+
+### Languages
+
+The dataset contains examples from all Wikipedia languages.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance is an image, its representation in bytes, a pre-computed embedding, and the set of captions attached to the image in Wikipedia.
+
+```
+{
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x225 at 0x7F88F3876358>,
+  'image_url': 'https://upload.wikimedia.org/wikipedia/commons/8/8b/Scolopendra_gigantea.jpg',
+  'embedding': [1.4784087, 2.8710432, 0.0, 0.51603067, ..., 10.266883, 0.51142216, 0.0, 2.3464653],
+  'metadata_url': 'http://commons.wikimedia.org/wiki/File:Scolopendra_gigantea.jpg',
+  'original_height': 3000,
+  'original_width': 4000,
+  'mime_type': 'image/jpeg',
+  'caption_attribution_description': 'English: Puerto Rican Giant Centipede, Scolopendra gigantea; Vieques, Puerto Rico Slovenčina: Stonožka obrovská, Scolopendra gigantea; Vieques, Portoriko',
+  'wit_features': {
+    'language': ['ro', 'vi', 'sk', ..., 'nl', 'th', 'lv'],
+    'page_url': ['https://ro.wikipedia.org/wiki/Scolopendra_gigantea', 'https://vi.wikipedia.org/wiki/Scolopendra_gigantea', 'https://sk.wikipedia.org/wiki/Scolopendra_gigantea', ..., 'https://nl.wikipedia.org/wiki/Scolopendra_gigantea', 'https://th.wikipedia.org/wiki/%E0%B8%95%E0%B8%B0%E0%B8%82%E0%B8%B2%E0%B8%9A%E0%B8%A2%E0%B8%B1%E0%B8%81%E0%B8%A9%E0%B9%8C%E0%B8%82%E0%B8%B2%E0%B9%80%E0%B8%AB%E0%B8%A5%E0%B8%B7%E0%B8%AD%E0%B8%87%E0%B9%80%E0%B8%9B%E0%B8%A3%E0%B8%B9', 'https://lv.wikipedia.org/wiki/Skolopendru_dzimta'],
+    'attribution_passes_lang_id': [True, True, True, ..., True, True, True],
+    'caption_alt_text_description': [None, None, None, ..., 'Scolopendra gigantea', None, 'Milzu skolopendra (Scolopendra gigantea)'],
+    'caption_reference_description': [None, None, None, ..., None, None, 'Milzu skolopendra (Scolopendra gigantea)'],
+    'caption_title_and_reference_description': [None, 'Scolopendra gigantea [SEP] ', None, ..., 'Scolopendra gigantea [SEP] ', None, 'Skolopendru dzimta [SEP] Milzu skolopendra (Scolopendra gigantea)'],
+    'context_page_description': ['Scolopendra gigantea este un miriapod din clasa Chilopoda, fiind cel mai mare reprezentant al genului Scolopendra. Adultul poate atinge o lungime de 26 cm, uneori depășind 30 cm. Această specie habitează în regiunile de nord și de vest a Americii de Sud, pe insulele Trinidad, insulele Virgine, Jamaica Hispaniola ș.a. Localnicii denumesc scolopendra chilopodul gigant galben și chilopodul gigant amazonian.', 'Scolopendra gigantea là đại diện lớn nhất của chi Scolopendra nói riêng và cả lớp rết nói chung, thường đạt độ dài 26 cm và có thể vượt quá 30 cm. Sinh sống ở khu vực phía bắc và tây của Nam Mỹ và các đảo Trinidad, Puerto Rico, Saint Thomas, U.S. Virgin Islands, Jamaica, và Hispaniola.', 'Scolopendra gigantea, starší slovenský nazov: štípavica veľká, je živočích z rodu Scolopendra, s veľkosťou do 30 cm.', ..., 'Scolopendra gigantea is een tijgerduizendpoot uit Zuid-Amerika. De soort jaagt onder andere op grote geleedpotigen, amfibieën, reptielen en kleine zoogdieren. Het is voor zover bekend de grootste niet uitgestorven duizendpoot ter wereld.', 'ตะขาบยักษ์ขาเหลืองเปรู หรือ ตะขาบยักษ์อเมซอน เป็นตะขาบชนิดที่มีขนาดใหญ่ที่สุดในสกุล Scolopendra โดยปกติเมื่อโตเต็มที่จะยาว 26 เซนติเมตร แต่บางครั้งก็สามารถโตได้ถึง 30 เซนติเมตร ตะขาบชนิดนี้อาศัยอยู่ทางแถบเหนือและตะวันต���ของทวีปอเมริกาใต้ และตามเกาะแก่งของประเทศตรินิแดดและจาไมกา เป็นสัตว์กินเนื้อ โดยกินจิ้งจก, กบ, นก, หนู และแม้แต่ค้างคาวเป็นอาหาร และขึ้นชื่อในเรื่องความดุร้าย', 'Skolpendru dzimta pieder pie simtkāju kārtas. Ap 400 dzimtas sugas sastopamas visā pasaulē, īpaši subtropu un tropu apgabalos. Mitinās augsnē, nobirušās lapās, plaisās, spraugās.'], 
+    'context_section_description': [None, 'Scolopendra gigantea (còn được gọi là Rết chân vàng khổng lồ Peru và Rết khổng lồ Amazon) là đại diện lớn nhất của chi Scolopendra nói riêng và cả lớp rết nói chung, thường đạt độ dài 26\xa0cm (10\xa0in) và có thể vượt quá 30\xa0cm (12\xa0in). Sinh sống ở khu vực phía bắc và tây của Nam Mỹ và các đảo Trinidad, Puerto Rico, Saint Thomas, U.S. Virgin Islands, Jamaica, và Hispaniola.', None, ..., 'Scolopendra gigantea is een tijgerduizendpoot uit Zuid-Amerika. De soort jaagt onder andere op grote geleedpotigen, amfibieën, reptielen en kleine zoogdieren. Het is voor zover bekend de grootste niet uitgestorven duizendpoot ter wereld.', None, 'Skolpendru dzimta (Scolopendridae) pieder pie simtkāju kārtas. Ap 400 dzimtas sugas sastopamas visā pasaulē, īpaši subtropu un tropu apgabalos. Mitinās augsnē, nobirušās lapās, plaisās, spraugās.'],
+    'hierarchical_section_title': ['Scolopendra gigantea', 'Scolopendra gigantea', 'Scolopendra gigantea', ..., 'Scolopendra gigantea', 'ตะขาบยักษ์ขาเหลืองเปรู', 'Skolopendru dzimta'],
+    'is_main_image': [True, True, True, ..., True, True, True], 
+    'page_title': ['Scolopendra gigantea', 'Scolopendra gigantea', 'Scolopendra gigantea', ..., 'Scolopendra gigantea', 'ตะขาบยักษ์ขาเหลืองเปรู', 'Skolopendru dzimta'], 
+    'section_title': [None, None, None, ..., None, None, None]
+  }
+}
+```
+
+**Note**: The dataset is stored in Parquet for better performance. This dataset was generated from the original files using [this script](wit_base/blob/main/scripts/wit.py). Additionally, 120 examples from the original files have incorrectly formatted one or more of the following fields: `original_height`, `original_width`, `mime_type` and `caption_attribution_description`. The fixed versions of these examples that were used in the generation script can be found [here](wit_base/blob/main/scripts/corrected_examples.py).
+
+### Data Fields
+
+- `image`: A `PIL.Image.Image` object containing the image resized to a width of 300-px while preserving its aspect ratio. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
+- `image_url`: URL to wikipedia image
+- `embedding`: Precomputed image embedding. Each image is described with a 2048-dimensional signature extracted from the second-to-last layer of a [ResNet-50](https://arxiv.org/abs/1512.03385) neural network trained with [Imagenet](https://www.image-net.org/) data. These embeddings contain rich information about the image content and layout, in a compact form
+- `metadata_url`: URL to wikimedia page containing the image and the metadata
+- `original_height`: Original image height before resizing
+- `original_width`: Original image width before resizing
+- `mime_type`: Mime type associated to the image
+- `caption_attribution_description`:  This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias.
+- `wit_features`: Sequence of captions for the image with language, page URL, information about the page, caption text, etc.
+  - `language`: Language code depicting wikipedia language of the page
+  - `page_url`: URL to wikipedia page
+  - `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description.
+  - `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers
+  - `caption_reference_description`: This is the caption that is visible on the wikipedia page directly below the image.
+  - `caption_title_and_reference_description`: Concatenation of `page_title` and `caption_reference_description`.
+  - `context_page_description`: Corresponds to the short description of the page. It provides a concise explanation of the scope of the page.
+  - `context_section_description`: Text within the image's section
+  - `hierarchical_section_title`: Hierarchical section's title
+  - `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.
+  - `page_changed_recently`: [More Information Needed] 
+  - `page_title`: Wikipedia page's title
+  - `section_title`: Section's title
+
+<p align='center'>
+  <img width='75%' src='https://production-media.paperswithcode.com/datasets/Screenshot_2021-03-04_at_14.26.02.png' alt="Half Dome" /> </br>
+<b>Figure: WIT annotation example. </b>
+</p>
+
+Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913)
+
+### Data Splits
+
+All data is held in `train` split, with a total of 6477255 examples.
+
+## Dataset Creation
+
+### Curation Rationale
+
+From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/): 
+
+> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images.
+
+> Getting easy access to the image files is crucial for participants to successfully develop competitive models.
+ 
+> With this large release of visual data, we aim to help the competition participants—as well as researchers and practitioners who are interested in working with Wikipedia images—find and download the large number of image files associated with the challenge, in a compact form.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+From the [paper, section 3.1](https://arxiv.org/abs/2103.01913):
+
+> We started with all Wikipedia content pages (i.e., ignoring other
+pages that have discussions, comments and such). These number about ~124M pages across 279 languages.
+
+#### Who are the source language producers?
+
+Text was extracted from Wikipedia.
+
+### Annotations
+
+#### Annotation process
+
+WIT was constructed using an automatic process. However it was human-validated.
+
+From the [paper, section 3.7](https://arxiv.org/abs/2103.01913):
+
+> To further verify the quality of the WIT dataset we performed a
+study using (crowd-sourced) human annotators. As seen in Fig. 3,
+we asked raters to answer 3 questions. Given an image and the page
+title, raters first evaluate the quality of the attribution description
+and reference description in the first two questions (order randomized). The third question understands the contextual quality of these
+text descriptions given the page description and caption. Each response is on a 3-point scale: "Yes" if the text perfectly describes
+the image, "Maybe" if it is sufficiently explanatory and "No" if it is
+irrelevant or the image is inappropriate.
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/#FN1):
+
+> For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the [RetinaFace](https://arxiv.org/abs/1905.00641) detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are [candidate for deletion](https://commons.wikimedia.org/wiki/Commons:Deletion_requests) on Commons from the dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+From the [paper, section 3.4](https://arxiv.org/abs/2103.01913):
+
+> Lastly we found that certain image-text pairs occurred very
+frequently. These were often generic images that did not have
+much to do with the main article page. Common examples
+included flags, logos, maps, insignia and such. To prevent
+biasing the data, we heavily under-sampled all such images
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+Miriam Redi, Fabian Kaelin and Tiziano Piccardi.
+
+### Licensing Information
+
+[CC BY-SA 4.0 international license](https://creativecommons.org/licenses/by-sa/4.0/)
+
+### Citation Information
+
+```bibtex
+@article{srinivasan2021wit,
+  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
+  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
+  journal={arXiv preprint arXiv:2103.01913},
+  year={2021}
+}
+```
+
+### Contributions
+
+Thanks to [@nateraw](https://github.com/nateraw), [yjernite](https://github.com/yjernite) and [mariosasko](https://github.com/mariosasko) for adding this dataset.