Upload batch 352 (20 files, last=huggingface_dataset/Dataset_Card/Gaborandi_diabetes_mellitus_type2_pubmed_abstracts.md)

huggingface_dataset/Dataset_Card/3ee_regularization-landscape.md

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

huggingface_dataset/Dataset_Card/BumblingOrange_Hanks_Embeddings.md

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+---
+license: bigscience-bloom-rail-1.0
+---
+This is a collection of embeddings that I decided to make public. Additionally, it will be where I host any future embeddings I decide to train.

huggingface_dataset/Dataset_Card/Datatang_Chinese_Speech_Data_by_Mobile_Phone.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for Datatang/Chinese_Speech_Data_by_Mobile_Phone
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://bit.ly/3tRtYLA
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The dataset contains 200 Chinese native speakers, covering main dialect zones. It is recorded in both noisy and quiet environment and more suitable for the actual application scenario for speech recognition. The recordings are commonly used spoken sentences. Texts are transcribed by professional annotators. It can be used for speech recognition and machine translation.
+                           
+For more details, please refer to the link: https://bit.ly/3tRtYLA
+
+### Supported Tasks and Leaderboards
+
+automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
+
+### Languages
+
+Chinese Mandarin
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing  
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions

huggingface_dataset/Dataset_Card/GEM_turku_hockey_data2text.md

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+---
+annotations_creators:
+- expert-created
+language_creators:
+- unknown
+language:
+- fi
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- unknown
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- table-to-text
+task_ids: []
+pretty_name: turku_hockey_data2text
+tags:
+- data-to-text
+---
+
+# Dataset Card for GEM/turku_hockey_data2text
+
+## Dataset Description
+
+- **Homepage:** https://turkunlp.org/hockey_data2text.html
+- **Repository:** https://github.com/TurkuNLP/Turku-hockey-data2text
+- **Paper:** https://aclanthology.org/W19-6125/
+- **Leaderboard:** N/A
+- **Point of Contact:** Jenna Kanerva, Filip Ginter
+
+### Link to Main Data Card
+
+You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/turku_hockey_data2text).
+
+### Dataset Summary 
+
+This is a Finnish data-to-text dataset in which the input is structured information about a hockey game and the output a description of the game.
+
+You can load the dataset via:
+```
+import datasets
+data = datasets.load_dataset('GEM/turku_hockey_data2text')
+```
+The data loader can be found [here](https://huggingface.co/datasets/GEM/turku_hockey_data2text).
+
+#### website
+[Website](https://turkunlp.org/hockey_data2text.html)
+
+#### paper
+[ACL anthology](https://aclanthology.org/W19-6125/)
+
+#### authors
+Jenna Kanerva, Samuel Rönnqvist, Riina Kekki, Tapio Salakoski, Filip Ginter (TurkuNLP / University of Turku)
+
+## Dataset Overview
+
+### Where to find the Data and its Documentation
+
+#### Webpage
+
+<!-- info: What is the webpage for the dataset (if it exists)? -->
+<!-- scope: telescope -->
+[Website](https://turkunlp.org/hockey_data2text.html)
+
+#### Download
+
+<!-- info: What is the link to where the original dataset is hosted? -->
+<!-- scope: telescope -->
+[Github](https://github.com/TurkuNLP/Turku-hockey-data2text)
+
+#### Paper
+
+<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
+<!-- scope: telescope -->
+[ACL anthology](https://aclanthology.org/W19-6125/)
+
+#### BibTex
+
+<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
+<!-- scope: microscope -->
+```
+@inproceedings{kanerva2019newsgen,
+  Title = {Template-free Data-to-Text Generation of Finnish Sports News},
+  Author = {Jenna Kanerva and Samuel R{\"o}nnqvist and Riina Kekki and Tapio Salakoski and Filip Ginter},
+  booktitle = {Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19)},
+  year={2019}
+  }
+```
+
+#### 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 -->
+Jenna Kanerva, Filip Ginter
+
+#### Contact Email
+
+<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+jmnybl@utu.fi, figint@utu.fi
+
+#### 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 Dialects
+
+<!-- info: What dialects are covered? Are there multiple dialects per language? -->
+<!-- scope: periscope -->
+written standard language
+
+#### Covered Languages
+
+<!-- quick -->
+<!-- info: What languages/dialects are covered in the dataset? -->
+<!-- scope: telescope -->
+`Finnish`
+
+#### Whose Language?
+
+<!-- info: Whose language is in the dataset? -->
+<!-- scope: periscope -->
+The original news articles are written by professional journalists. The text passages extracted in the annotation may be slightly edited compared to the original language during the corpus annotation.
+
+#### License
+
+<!-- quick -->
+<!-- info: What is the license of the dataset? -->
+<!-- scope: telescope -->
+cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International
+
+#### Intended Use
+
+<!-- info: What is the intended use of the dataset? -->
+<!-- scope: microscope -->
+This dataset was developed as a benchmark for evaluating template-free, machine learning methods on Finnish news generation in the area of ice hockey reporting.
+
+#### Primary Task
+
+<!-- info: What primary task does the dataset support? -->
+<!-- scope: telescope -->
+Data-to-Text
+
+#### Communicative Goal
+
+<!-- quick -->
+<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
+<!-- scope: periscope -->
+Describe an event from an ice hockey game based on the given structural data.
+
+
+### 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 -->
+University of Turku
+
+#### Dataset Creators
+
+<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
+<!-- scope: microscope -->
+Jenna Kanerva, Samuel Rönnqvist, Riina Kekki, Tapio Salakoski, Filip Ginter (TurkuNLP / University of Turku)
+
+#### Funding
+
+<!-- info: Who funded the data creation? -->
+<!-- scope: microscope -->
+The project was supported by the Google Digital News Innovation Fund.
+
+#### 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 -->
+Jenna Kanerva, Filip Ginter (TurkuNLP / University of Turku)
+
+
+### Dataset Structure
+
+#### Data Fields
+
+<!-- info: List and describe the fields present in the dataset. -->
+<!-- scope: telescope -->
+The dataset is constructed of games, where each game is a list of events. If the event was annotated (corresponding sentence was found from the news article), it includes `text` field with value other than empty string ("").
+
+For each game (dict), there are keys `gem_id` (string), `id` (string), `news_article` (string), and `events` (list).
+
+For each event (dict), there are different, relevant keys available with non empty values depending on the event type (e.g. goal or penalty). The mandatory keys for each event are `event_id` (string), `event_type` (string), `text` (string, empty string if not annotated), and `multi_reference` (bool). The keys not relevant for the specific event type are left empty.
+
+The relevant keys in the event dictionary are:
+
+For each event type, the following keys are relevant:
+  `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
+  `event_type`: Type of the event, possible values are `game result`, `goal`, `penalty`, or `saves` (string)
+  `text`: Natural language description of the event, or empty string if not available (string)
+  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
+
+
+The rest of the fields are specific to the event type. The relevant fields for each event type are:
+
+game result:
+  `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
+  `event_type`: Type of the event (string)
+  `home_team`: Name of the home team (string)
+  `guest_team`: Name of the guest team (string)
+  `score`: Final score of the game, in the form of home–guest (string)
+  `periods`: Scores for individual periods, each in the form of home–guest score in that period (list of strings)
+  `features`: Additional features, such as overtime win or shoot out (list of strings)
+  `text`: Natural language description of the event, or empty string if not available (string)
+  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
+
+goal:
+  `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
+  `event_type`: Type of the event (string)
+  `player`: Name of the player scoring (string)
+  `assist`: Names of the players assisting, at most two players (list of strings)
+  `team`: Team scoring with possible values of `home` or `guest` (string)
+  `team_name`: Name of the team scoring (string)
+  `score`: Score after the goal, in the form of home–guest (string)
+  `time`: Time of the goal, minutes and seconds from the beginning (string)
+  `features`: Additional features, such as power play or short-handed goal (list of strings)
+  `text`: Natural language description of the event, or empty string if not available (string)
+  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
+
+penalty:
+  `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
+  `event_type`: Type of the event (string)
+  `player`: Name of the player getting the penalty (string)
+  `team`: Team getting the penalty with possible values of `home` or `guest` (string)
+  `team_name`: Name of the team getting the penalty (string)
+  `penalty_minutes`: Penalty minutes (string)
+  `time`: Time of the penalty, minutes and seconds from the beginning (string)
+  `text`: Natural language description of the event, or empty string if not available (string)
+  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
+
+saves:
+ `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
+  `event_type`: Type of the event (string)
+  `player`: Name of the goalkeeper (string)
+  `team`: Team of the goalkeeper with possible values of `home` or `guest` (string)
+  `team_name`: Name of the team (string)
+  `saves`: Number of saves in the game (string)
+  `text`: Natural language description of the event, or empty string if not available (string)
+  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
+
+
+Text passages describing multiple events (multi_reference):
+
+Some text passages refer to multiple events in such way that separating them to individual statements is not adequate (e.g. "The home team received two penalties towards the end of the first period."). In these cases, multiple events are aligned to the same text passage so that the first event (in chronological order) include the annotated text passage, while the rest of the events referring to the same text passage include the identifier of the first event in the annotated text field (e.g. `text`: "E4").
+
+#### Example Instance
+
+<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
+<!-- scope: periscope -->
+```
+{
+  'gem_id': 'gem-turku_hockey_data2text-train-0',
+  'id': '20061031-TPS-HPK',
+  'news_article': 'HPK:n hyvä syysvire jatkuu jääkiekon SM-liigassa. Tiistaina HPK kukisti mainiolla liikkeellä ja tehokkaalla ylivoimapelillä TPS:n vieraissa 1–0 (1–0, 0–0, 0–0).\nHPK hyödynsi ylivoimaa mennen jo ensimmäisessä erässä Mikko Mäenpään maalilla 1–0 -johtoon.\nToisessa ja kolmannessa erässä HPK tarjosi edelleen TPS:lle runsaasti tilanteita, mutta maalia eivät turkulaiset millään ilveellä saaneet. Pahin este oli loistavan pelin Hämeenlinnan maalilla pelannut Mika Oksa.\nTPS:n maalissa Jani Hurme ei osumille mitään mahtanut. Joukkueen suuri yksinäinen kenttäpelaaja oli Kai Nurminen, mutta hänelläkään ei ollut onnea maalitilanteissa.',
+  'events':
+    {
+      'event_id': ['E1', 'E2', 'E3'],
+      'event_type': ['game result', 'penalty', 'goal'],
+      'text': ['HPK kukisti TPS:n vieraissa 1–0 (1–0, 0–0, 0–0).', '', 'HPK hyödynsi ylivoimaa mennen jo ensimmäisessä erässä Mikko Mäenpään maalilla 1–0 -johtoon.'],
+      'home_team': ['TPS', '', ''],
+      'guest_team': ['HPK', '', ''],
+      'score': ['0–1', '', '0–1'],
+      'periods': [['0–1', '0–0', '0–0'], [], []],
+      'features': [[], [], ['power play']],
+      'player': ['', 'Fredrik Svensson', 'Mikko Mäenpää'],
+      'assist': [[], [], ['Jani Keinänen', 'Toni Mäkiaho']],
+      'team': ['', 'guest', 'guest'],
+      'team_name': ['', 'HPK', 'HPK'],
+      'time': ['', '9.28', '14.57'],
+      'penalty_minutes': ['', '2', ''],
+      'saves': ['', '', ''],
+      'multi_reference': [false, false, false]
+    }
+}
+```
+
+#### Data Splits
+
+<!-- info: Describe and name the splits in the dataset if there are more than one. -->
+<!-- scope: periscope -->
+The corpus include 3 splits: train, validation, and test.
+
+
+
+## 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 -->
+The dataset was created to develop machine learned text generation models for Finnish ice hockey news, where the generation would reflect the natural language variation found from the game reports written by professional journalists. While the original game reports often include additional information not derivable from the game statistics, the corpus was fully manually curated to remove all such  information from the natural language descriptions. The rationale of such curation was to prevent model 'hallucinating' additional facts.
+
+#### 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 -->
+yes
+
+#### Difference from other GEM datasets
+
+<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
+<!-- scope: microscope -->
+This is the only data2text corpus for Finnish in GEM.
+
+#### Ability that the Dataset measures
+
+<!-- info: What aspect of model ability can be measured with this dataset? -->
+<!-- scope: periscope -->
+morphological inflection, language variation
+
+
+### 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 -->
+`data points modified`
+
+#### Modification Details
+
+<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
+<!-- scope: microscope -->
+Structural data was translated into English.
+
+#### Additional Splits?
+
+<!-- info: Does GEM provide additional splits to the dataset? -->
+<!-- scope: telescope -->
+no
+
+
+### Getting Started with the Task
+
+
+
+
+## Previous Results
+
+### Previous Results
+
+#### Metrics
+
+<!-- info: What metrics are typically used for this task? -->
+<!-- scope: periscope -->
+`BLEU`, `METEOR`, `ROUGE`, `WER`
+
+#### 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 -->
+Automatic evaluation: BLEU, NIST, METEOR, ROUGE-L, CIDEr
+Manual evaluation: factual mistakes, grammatical errors, minimum edit distance to an acceptable game report (using WER)
+
+#### Previous results available?
+
+<!-- info: Are previous results available? -->
+<!-- scope: telescope -->
+yes
+
+
+
+## Dataset Curation
+
+### Original Curation
+
+#### Original Curation Rationale
+
+<!-- info: Original curation rationale -->
+<!-- scope: telescope -->
+The dataset is designed for text generation (data2text), where the original source of natural language descriptions is news articles written by journalists. While the link between structural data (ice hockey game statistics) and the news articles describing the game  was quite weak (news articles including a lot of information not derivable from the statistics, while leaving many events unmentioned), the corpus includes full manual annotation aligning the events extracted from game statistics and the corresponding natural language passages extracted from the news articles.
+
+Each event is manually aligned into a sentence-like passage, and in case a suitable passage was not found, the annotation is left empty (with value `None`). The extracted passages were manually modified not to include additional information not derivable from the game statistics, or not considered as world knowledge. The manual curation of passages is designed to prevent model hallucination, i.e. model learning to generate facts not derivable from the input data.
+
+#### Communicative Goal
+
+<!-- info: What was the communicative goal? -->
+<!-- scope: periscope -->
+Describing the given events (structural data) in natural language, and therefore generating ice hockey game reports.
+
+#### 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 -->
+`Other`
+
+#### Language Producers
+
+<!-- info: What further information do we have on the language producers? -->
+<!-- scope: microscope -->
+The initial data, both game statistics and news articles, were obtained from the Finnish News Agency STT news archives released for academic use (http://urn.fi/urn:nbn:fi:lb-2019041501). The original news articles are written by professional journalists.
+
+We (TurkuNLP) gratefully acknowledge the collaboration of Maija Paikkala, Salla Salmela and Pihla Lehmusjoki from the Finnish News Agency STT while creating the corpus.
+
+#### Topics Covered
+
+<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
+<!-- scope: periscope -->
+Ice hockey, news
+
+#### Data Validation
+
+<!-- info: Was the text validated by a different worker or a data curator? -->
+<!-- scope: telescope -->
+not validated
+
+#### Was Data Filtered?
+
+<!-- info: Were text instances selected or filtered? -->
+<!-- scope: telescope -->
+algorithmically
+
+#### Filter Criteria
+
+<!-- info: What were the selection criteria? -->
+<!-- scope: microscope -->
+Include only games, where both game statistics and a news article describing the game were available (based on timestamps and team names).
+
+
+### Structured Annotations
+
+#### Additional Annotations?
+
+<!-- quick -->
+<!-- info: Does the dataset have additional annotations for each instance? -->
+<!-- scope: telescope -->
+expert created
+
+#### Number of Raters
+
+<!-- info: What is the number of raters -->
+<!-- scope: telescope -->
+1
+
+#### Rater Qualifications
+
+<!-- info: Describe the qualifications required of an annotator. -->
+<!-- scope: periscope -->
+Members of the TurkuNLP research group, native speakers of Finnish.
+
+#### Raters per Training Example
+
+<!-- info: How many annotators saw each training example? -->
+<!-- scope: periscope -->
+1
+
+#### Raters per Test Example
+
+<!-- info: How many annotators saw each test example? -->
+<!-- scope: periscope -->
+1
+
+#### Annotation Service?
+
+<!-- info: Was an annotation service used? -->
+<!-- scope: telescope -->
+no
+
+#### Annotation Values
+
+<!-- info: Purpose and values for each annotation -->
+<!-- scope: microscope -->
+Manual alignment of events and their natural language descriptions. Removing information not derivable from the input data or world knowledge in order to prevent the model 'hallucination'.
+
+#### Any Quality Control?
+
+<!-- info: Quality control measures? -->
+<!-- scope: telescope -->
+validated by data curators
+
+#### Quality Control Details
+
+<!-- info: Describe the quality control measures that were taken. -->
+<!-- scope: microscope -->
+Manual inspection of examples during the initial annotation training phrase.
+
+
+### 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 -->
+The corpus license was agreed with the providers of the source material.
+
+
+### 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 -->
+yes/very likely
+
+#### 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 -->
+no
+
+
+### 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 -->
+The dataset represents only written standard language. 
+
+
+
+## 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 -->
+None
+
+
+### 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 -->
+`non-commercial use only`
+
+#### 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 -->
+`non-commercial use only`
+
+
+### Known Technical Limitations
+
+
+

huggingface_dataset/Dataset_Card/Gaborandi_diabetes_mellitus_type2_pubmed_abstracts.md

@@ -0,0 +1,4 @@
+- This Dataset has been downloaded from PubMed
+- It has abstracts and titles that are related to type 2 DM
+- the data has been cleaned before uploading
+- it could be used for any NLP task, such as Domain Adaptation

huggingface_dataset/Dataset_Card/Nerfgun3_enaic31_LoRA.md

@@ -0,0 +1,76 @@
+---
+language:
+- en
+license: creativeml-openrail-m
+thumbnail: "https://huggingface.co/datasets/Nerfgun3/enaic31_LoRA/resolve/main/preview/Preview%20(1).png"
+tags:
+- stable-diffusion
+- text-to-image
+- image-to-image
+inference: false
+---
+
+# Enaic31 Artstyle LoRA
+
+# Use Cases
+
+The LoRA is in itself very compatible with the most diverse model. However, it is most effective when used with Kenshi or AbyssOrangeMix2.
+
+The LoRA itself was trained with the token: ```skistyle```.
+I would suggest using the token with AbyssOrangeMix2, but not with Kenshi, since I got better results that way.
+
+The models mentioned right now
+1. AbyssOrangeMix2 from [WarriorMama777](https://huggingface.co/WarriorMama777/OrangeMixs)
+2. Kenshi Model from [Luna](https://huggingface.co/SweetLuna/Kenshi)
+
+## Strength
+
+I would personally use these strength with the assosiated model:
+
+Soft-Version:
+- 0.6-0.85 for AbyssOrangeMix2
+- 0.5-0.75 for Kenshi
+
+Hard-Version:
+- 0.4-0.6 for AbyssOrangeMix2
+- 0.3-0.55 for Kenshi
+
+# Showcase
+
+**Example 1**
+
+<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/enaic31_LoRA/resolve/main/preview/Preview%20(2).png"/>
+
+```
+skistyle,
+1girl, solo, animal ears, long hair, looking at viewer, bell, upper body, bangs, closed mouth, animal ear fluff, hair between eyes, grey eyes, blush, grey hair, cat ears, neck bell, shirt,
+Steps: 32, Sampler: Euler a, CFG scale: 7
+```
+
+**Example 2**
+<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/enaic31_LoRA/resolve/main/preview/Preview%20(3).png"/>
+
+```
+skistyle,
+1girl, solo, animal ears, long hair, looking at viewer, bell, upper body, bangs, closed mouth, animal ear fluff, hair between eyes, grey eyes, blush, grey hair, cat ears, neck bell, shirt,
+Steps: 32, Sampler: Euler a, CFG scale: 7
+```
+
+**Example 3**
+<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/enaic31_LoRA/resolve/main/preview/Preview%20(4).png"/>
+
+```
+skistyle,
+small breasts, dark-skinned female, shorts, dark skin, hair ornament, black hair, smile, glasses, v, cleavage, hairclip, brown hair, grin, aged up, brown eyes, white background, 1girl, looking at viewer, off shoulder, shirt, sweater, simple background, short shorts, denim shorts
+Steps: 32, Sampler: Euler a, CFG scale: 7
+```
+
+# License
+
+This model 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 model 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/NoraAlt_Mawqif_Stance-Detection.md

@@ -0,0 +1,56 @@
+---
+task_categories:
+- text-classification
+language:
+- ar
+pretty_name: 'Mawqif: Stance Detection'
+size_categories:
+- 1K<n<10K
+tags:
+- Stance Detection
+- Sentiment Analysis
+- Sarcasm Detection
+---
+# Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection
+
+- *Mawqif* is the first Arabic dataset that can be used for target-specific stance detection. 
+
+- This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.
+
+- We benchmark *Mawqif* dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89\%.
+
+
+
+# Mawqif Statistics
+
+- This dataset consists of **4,121** tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities.
+
+- The following figure illustrates the labels’ distribution across all targets, and the distribution per target.
+
+<img width="738" alt="dataStat-2" src="https://user-images.githubusercontent.com/31368075/188299057-54d04e87-802d-4b0e-b7c6-56bdc1078284.png">
+
+
+
+# Interactive Visualization
+
+To browse an interactive visualization of the *Mawqif* dataset, please click [here](https://public.tableau.com/views/MawqifDatasetDashboard/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link)
+- *You can click on visualization components to filter the data by target and by class. **For example,** you can click on “women empowerment" and "against" to get the information of tweets that express against women empowerment.* 
+
+
+# Citation
+If you feel our paper and resources are useful, please consider citing our work!
+```
+@inproceedings{alturayeif-etal-2022-mawqif,
+    title = "Mawqif: A Multi-label {A}rabic Dataset for Target-specific Stance Detection",
+    author = "Alturayeif, Nora Saleh  and
+      Luqman, Hamzah Abdullah  and
+      Ahmed, Moataz Aly Kamaleldin",
+    booktitle = "Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.wanlp-1.16",
+    pages = "174--184"
+}
+```

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__hindsight-neglect-10shot-inverse-scali-383fe9-1695459606.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- inverse-scaling/hindsight-neglect-10shot
+eval_info:
+  task: text_zero_shot_classification
+  model: inverse-scaling/opt-125m_eval
+  metrics: []
+  dataset_name: inverse-scaling/hindsight-neglect-10shot
+  dataset_config: inverse-scaling--hindsight-neglect-10shot
+  dataset_split: train
+  col_mapping:
+    text: prompt
+    classes: classes
+    target: answer_index
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: inverse-scaling/opt-125m_eval
+* Dataset: inverse-scaling/hindsight-neglect-10shot
+* Config: inverse-scaling--hindsight-neglect-10shot
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__examplei-all-929d48-1748861030.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- phpthinh/examplei
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-1b1
+  metrics: ['f1']
+  dataset_name: phpthinh/examplei
+  dataset_config: all
+  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-1b1
+* Dataset: phpthinh/examplei
+* Config: all
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-squad-6abc415f-12465657.md

@@ -0,0 +1,35 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad
+eval_info:
+  task: extractive_question_answering
+  model: deepset/deberta-v3-large-squad2
+  metrics: []
+  dataset_name: squad
+  dataset_config: plain_text
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: deepset/deberta-v3-large-squad2
+* Dataset: squad
+* Config: plain_text
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model.

huggingface_dataset/Dataset_Card/collectivat_una-fraza-al-diya.md

@@ -0,0 +1,44 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- lad
+- es
+- tr
+- en
+license: cc-by-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-generation
+- translation
+task_ids:
+- language-modeling
+---
+
+# Una fraza al diya
+
+Ladino language learning sentences prepared by Karen Sarhon of Sephardic Center of Istanbul. Each sentence has translations in Turkish, English, Spanish. Includes audio and image. 307 sentences in total.
+
+Source: https://sefarad.com.tr/judeo-espanyolladino/frazadeldia/
+
+Images and audio: http://collectivat.cat/share/judeoespanyol_audio_image.zip 
+
+[Offical link on Ladino Data Hub](https://data.sefarad.com.tr/dataset/una-fraza-al-diya-skad)
+
+Paper on [ArXiv](https://arxiv.org/abs/2205.15599)
+
+Citation:
+
+```
+Preparing an endangered language for the digital age: The Case of Judeo-Spanish. Alp Öktem, Rodolfo Zevallos, Yasmin Moslem, Güneş Öztürk, Karen Şarhon. 
+Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @  LREC 2022. Marseille, France. 20 June 2022
+```
+
+This dataset is created as part of project "Judeo-Spanish: Connecting the two ends of the Mediterranean" carried out by Col·lectivaT and Sephardic Center of Istanbul within the framework of the “Grant Scheme for Common Cultural Heritage: Preservation and Dialogue between Turkey and the EU–II (CCH-II)” implemented by the Ministry of Culture and Tourism of the Republic of Turkey with the financial support of the European Union. The content of this website is the sole responsibility of Col·lectivaT and does not necessarily reflect the views of the European Union.

huggingface_dataset/Dataset_Card/hackathon-pln-es_poems-es.md

@@ -0,0 +1,11 @@
+---
+license: wtfpl
+---
+Dataset descargado de la página kaggle.com.
+El archivo original contenía información en inglés y posteriormente fue traducida para su uso.
+El dataset contiene las columnas:
+- Autor: corresponde al autor del poema.
+- Contenido: contiene todo el poema.
+- Nombre del poema: contiene el título del poema.
+- Años: corresponde al tiempo en que fue hecho el poema. 
+- Tipo: contiene el tipo que pertenece el poema.

huggingface_dataset/Dataset_Card/huggingartists_adele.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/adele"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of the generated dataset:** 0.304292 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/45ccf22bba4c1f80989e645c2fd4ec44.1000x1000x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/adele">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">Adele</div>
+    <a href="https://genius.com/artists/adele">
+    	<div style="text-align: center; font-size: 14px;">@adele</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/adele).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/adele")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|203|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/adele")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/joelito_german_argument_mining.md

@@ -0,0 +1,260 @@
+---
+annotations_creators:
+- expert-generated
+- found
+language_creators:
+- found
+language:
+- de
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: Annotated German Legal Decision Corpus
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+---
+
+# Dataset Card for Annotated German Legal Decision Corpus
+
+## Table of Contents
+
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:** https://zenodo.org/record/3936490#.X1ed7ovgomK
+- **Paper:** Urchs., S., Mitrović., J., & Granitzer., M. (2021). Design and Implementation of German Legal Decision
+  Corpora. Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
+  515–521. https://doi.org/10.5220/0010187305150521
+- **Leaderboard:**
+- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
+
+### Dataset Summary
+
+This dataset consists of 200 randomly chosen judgments. In these judgments a legal expert annotated the components
+conclusion, definition and subsumption of the German legal writing style Urteilsstil.
+
+*"Overall 25,075 sentences are annotated. 5% (1,202) of these sentences are marked as conclusion, 21% (5,328) as
+definition, 53% (13,322) are marked as subsumption and the remaining 21% (6,481) as other. The length of judgments in
+sentences ranges from 38 to 862 sentences. The median of judgments have 97 sentences, the length of most judgments is on
+the shorter side."* (Urchs. et al., 2021)
+
+*"Judgments from 22 of the 131 courts are selected for the corpus. Most judgments originate from the VG Augsburg (59 /
+30%) followed by the VG Ansbach (39 / 20%) and LSG Munich (33 / 17%)."* (Urchs. et al., 2021)
+
+*"29% (58) of all selected judgments are issued in the year 2016, followed by 22% (44) from the year 2017 and 21% (41)
+issued in the year 2015. [...] The percentages of selected judgments and decisions issued in 2018 and 2019 are roughly
+the same. No judgments from 2020 are selected."* (Urchs. et al., 2021)
+
+### Supported Tasks and Leaderboards
+
+The dataset can be used for multi-class text classification tasks, more specifically, for argument mining.
+
+### Languages
+
+The language in the dataset is German as it is used in Bavarian courts in Germany.
+
+## Dataset Structure
+
+### Data Instances
+
+Each sentence is saved as a json object on a line in one of the three files `train.jsonl`, `validation.jsonl`
+or `test.jsonl`. The file `meta.jsonl` contains meta information for each court. The `file_number` is present in all
+files for identification. Each sentence of the court decision was categorized according to its function.
+
+### Data Fields
+
+The file `meta.jsonl` contains for each row the following fields:
+
+- `meta_title`: Title provided by the website, it is used for saving the decision
+- `court`: Issuing court
+- `decision_style`: Style of the decision; the corpus contains either *Urteil* (='judgment') or *Endurteil* (
+  ='end-judgment')
+- `date`: Date when the decision was issued by the court
+- `file_number`: Identification number used for this decision by the court
+- `title`: Title provided by the court
+- `norm_chains`: Norms related to the decision
+- `decision_guidelines`: Short summary of the decision
+- `keywords`: Keywords associated with the decision
+- `lower_court`: Court that decided on the decision before
+- `additional_information`: Additional Information
+- `decision_reference`: References to the location of the decision in beck-online
+- `tenor`: Designation of the legal consequence ordered by the court (list of paragraphs)
+- `legal_facts`: Facts that form the base for the decision (list of paragraphs)
+
+The files `train.jsonl`, `validation.jsonl` and `test.jsonl` contain the following fields:
+
+- `file_number`: Identification number for linkage with the file `meta.jsonl`
+- `input_sentence`: The sentence to be classified
+- `label`: In depth explanation of the court decision. Each sentence is assigned to one of the major components of
+  German *Urteilsstil* (Urchs. et al., 2021) (list of paragraphs, each paragraph containing list of sentences, each
+  sentence annotated with one of the following four labels):
+  - `conclusion`: Overall result
+  - `definition`: Abstract legal facts and consequences
+  - `subsumption`: Determination sentence / Concrete facts
+  - `other`: Anything else
+- `context_before`: Context in the same paragraph before the input_sentence
+- `context_after`: Context in the same paragraph after the input_sentence
+
+### Data Splits
+
+No split provided in the original release.
+
+Splits created by Joel Niklaus. We randomly split the dataset into 80% (160 decisions, 19271 sentences) train, 10%
+validation (20 decisions, 2726 sentences) and 10% test (20 decisions, 3078 sentences). We made sure, that a decision
+only occurs in one split and is not dispersed over multiple splits.
+
+Label Distribution
+
+| label          |      train |   validation |      test |
+|:---------------|-----------:|-------------:|----------:|
+| conclusion     |        975 |          115 |       112 |
+| definition     |       4105 |          614 |       609 |
+| subsumption    |      10034 |         1486 |      1802 |
+| other          |       4157 |          511 |       555 |
+| total          |  **19271** |     **2726** |  **3078** |
+
+## Dataset Creation
+
+### Curation Rationale
+
+Creating a publicly available German legal text corpus consisting of judgments that have been annotated by a legal
+expert. The annotated components consist of *conclusion*, *definition* and *subsumption* of the German legal writing
+style *Urteilsstil*.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+*“The decision corpus is a collection of the decisions published on the website www.gesetze-bayern.de. At the time of
+the crawling the website offered 32,748 decisions of 131 Bavarian courts, dating back to 2015. The decisions are
+provided from the Bavarian state after the courts agreed to a publication. All decisions are processed by the publisher
+C.H.BECK, commissioned by the Bavarian state. This processing includes anonymisation, key-wording, and adding of
+editorial guidelines to the decisions.”* (Urchs. et al., 2021)
+
+#### Who are the source language producers?
+
+German courts from Bavaria
+
+### Annotations
+
+#### Annotation process
+
+*“As stated above, the judgment corpus consist of 200 randomly chosen judgments that are annotated by a legal expert,
+who holds a first legal state exam. Due to financial, staff and time reasons the presented iteration of the corpus was
+only annotated by a single expert. In a future version several other experts will annotate the corpus and the
+inter-annotator agreement will be calculated.”* (Urchs. et al., 2021)
+
+#### Who are the annotators?
+
+A legal expert, who holds a first legal state exam.
+
+### Personal and Sensitive Information
+
+*"All decisions are processed by the publisher C.H.BECK, commissioned by the Bavarian state. This processing includes **
+anonymisation**, key-wording, and adding of editorial guidelines to the decisions.”* (Urchs. et al., 2021)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+The SoMaJo Sentence Splitter has been used. Upon manual inspection of the dataset, we could see that the sentence
+splitter had poor accuracy in some cases (see ```analyze_dataset()``` in ```convert_to_hf_dataset.py```). When creating
+the splits, we thought about merging small sentences with their neighbors or removing them all together. However, since
+we could not find an straightforward way to do this, we decided to leave the dataset content untouched.
+
+Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton
+Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset
+consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the
+dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,
+differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to
+have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the
+original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to
+the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
+
+## Additional Information
+
+### Dataset Curators
+
+The names of the original dataset curators and creators can be found in references given below, in the section *Citation
+Information*. Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch)
+; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch)
+; [Github](https://github.com/kapllan)).
+
+### Licensing Information
+
+[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
+
+### Citation Information
+
+```
+@dataset{urchs_stefanie_2020_3936490,
+  author       = {Urchs, Stefanie and
+                  Mitrović, Jelena},
+  title        = {{German legal jugements annotated with judement 
+                   style components}},
+  month        = jul,
+  year         = 2020,
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.3936490},
+  url          = {https://doi.org/10.5281/zenodo.3936490}
+}
+```
+
+```
+@conference{icaart21,
+  author = {Urchs., Stefanie and Mitrovi{\'{c}}., Jelena and Granitzer., Michael},
+  booktitle = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
+  doi = {10.5220/0010187305150521},
+  isbn = {978-989-758-484-8},
+  issn = {2184-433X},
+  organization = {INSTICC},
+  pages = {515--521},
+  publisher = {SciTePress},
+  title = {{Design and Implementation of German Legal Decision Corpora}},
+  year = {2021}
+}
+```
+
+### Contributions
+
+Thanks to [@kapllan](https://github.com/kapllan) and [@joelniklaus](https://github.com/joelniklaus) for adding this
+dataset.

huggingface_dataset/Dataset_Card/lslattery_wafer-defect-detection.md

@@ -0,0 +1,2 @@
+Dataset used by the paper:
+Wu, Ming-Ju, Jyh-Shing R. Jang, and Jui-Long Chen. “Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets.” IEEE Transactions on Semiconductor Manufacturing 28, no. 1 (February 2015): 1–12.

huggingface_dataset/Dataset_Card/mad_IndonesiaNewsDataset.md

@@ -0,0 +1,21 @@
+# Indonesia News Dataset 
+
+## Source 
+
+- [Kompas](https://www.kompas.com/)
+- [Detik](https://www.detik.com/) (Cooming Soon)
+- [Liputan6](https://www.liputan6.com/) (Cooming Soon)
+- [Tempo](https://www.tempo.co/) (Cooming Soon)
+- [Okezone](https://www.okezone.com/) (Cooming Soon)
+- [CNN Indonesia](https://www.cnnindonesia.com/) (Cooming Soon)
+- [CNBC Indonesia](https://www.cnbcindonesia.com/) (Cooming Soon)
+
+## Roadmap 
+
+1. Web Data Crawling and Indexing 
+2. NER Model and Sentiment Analysis Model 
+3. Data Visualization
+
+## Contact 
+
+- You Can Contact Me at [twitter](https://twitter.com/skidipapGUY)

huggingface_dataset/Dataset_Card/mteb_bucc-bitext-mining.md

@@ -0,0 +1,40 @@
+---
+annotations_creators: []
+language_creators: []
+language:
+- de
+- en
+- fr
+- ru
+- zh
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+- multilingual
+pretty_name: MTEB Benchmark
+---
+
+# Dataset Card for MTEB Benchmark
+
+## Dataset Description
+
+- **Homepage:** https://github.com/embeddings-benchmark/mteb-draft
+- **Repository:** https://github.com/embeddings-benchmark/mteb-draft
+- **Paper:** soon
+- **Leaderboard:** https://docs.google.com/spreadsheets/d/14P8bdEzsIgTGGlp9oOlMw-THrQbn2fYfZEkZV4NUBos
+- **Point of Contact:** nouamane@huggingface.co
+
+### Dataset Summary
+
+MTEB is a heterogeneous benchmark that has been built from diverse tasks:
+* BitextMining: [BUCC](https://comparable.limsi.fr/bucc2018/bucc2018-task.html), [Tatoeba](https://github.com/facebookresearch/LASER/tree/main/data/tatoeba/v1)
+* Classification: [AmazonCounterfactualClassification](https://arxiv.org/abs/2104.06893), [AmazonPolarityClassification](https://dl.acm.org/doi/10.1145/2507157.2507163), [AmazonReviewsClassification](https://arxiv.org/abs/2010.02573), [Banking77Classification](https://arxiv.org/abs/2003.04807), [EmotionClassification](https://www.aclweb.org/anthology/D18-1404), [ImdbClassification](http://www.aclweb.org/anthology/P11-1015), [MassiveIntentClassification](https://arxiv.org/abs/2204.08582#:~:text=MASSIVE%20contains%201M%20realistic%2C%20parallel,diverse%20languages%20from%2029%20genera.), [MassiveScenarioClassification](https://arxiv.org/abs/2204.08582#:~:text=MASSIVE%20contains%201M%20realistic%2C%20parallel,diverse%20languages%20from%2029%20genera.), [MTOPDomainClassification](https://arxiv.org/pdf/2008.09335.pdf), [MTOPIntentClassification](https://arxiv.org/pdf/2008.09335.pdf), [ToxicConversationsClassification](https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification/overview), [TweetSentimentExtractionClassification](https://www.kaggle.com/competitions/tweet-sentiment-extraction/overview)
+* Clustering: [ArxivClusteringP2P](https://www.kaggle.com/Cornell-University/arxiv), [ArxivClusteringS2S](https://www.kaggle.com/Cornell-University/arxiv), [BiorxivClusteringP2P](https://api.biorxiv.org/), [BiorxivClusteringS2S](https://api.biorxiv.org/), [MedrxivClusteringP2P](https://api.biorxiv.org/), [MedrxivClusteringS2S](https://api.biorxiv.org/), [RedditClustering](https://arxiv.org/abs/2104.07081), [RedditClusteringP2P](https://huggingface.co/datasets/sentence-transformers/reddit-title-body), [StackExchangeClustering](https://arxiv.org/abs/2104.07081), [StackExchangeClusteringP2P](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl), [TwentyNewsgroupsClustering](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html)
+* Pair Classification: [SprintDuplicateQuestions](https://www.aclweb.org/anthology/D18-1131/), [TwitterSemEval2015](https://alt.qcri.org/semeval2015/task1/), [TwitterURLCorpus](https://languagenet.github.io/)
+* Reranking: [AskUbuntuDupQuestions](https://github.com/taolei87/askubuntu), [MindSmallReranking](https://www.microsoft.com/en-us/research/uploads/prod/2019/03/nl4se18LinkSO.pdf), [SciDocs](https://allenai.org/data/scidocs), [StackOverflowDupQuestions](https://www.microsoft.com/en-us/research/uploads/prod/2019/03/nl4se18LinkSO.pdf)
+* Retrieval: [ArguAna](http://argumentation.bplaced.net/arguana/data), [ClimateFEVER](https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html), [CQADupstackRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/), [DBPedia](https://github.com/iai-group/DBpedia-Entity/), [FEVER](https://fever.ai/), [FiQA2018](https://sites.google.com/view/fiqa/), [HotpotQA](https://hotpotqa.github.io/), [MSMARCO](https://microsoft.github.io/msmarco/), [MSMARCOv2](https://microsoft.github.io/msmarco/TREC-Deep-Learning.html), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/), [NQ](https://ai.google.com/research/NaturalQuestions/), [QuoraRetrieval](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs), [SCIDOCS](https://allenai.org/data/scidocs), [SciFact](https://github.com/allenai/scifact), [Touche2020](https://webis.de/events/touche-20/shared-task-1.html), [TRECCOVID](https://ir.nist.gov/covidSubmit/index.html)
+* STS: [BIOSSES](https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html), [SICK-R](https://www.aclweb.org/anthology/S14-2001.pdf), [STS12](https://www.aclweb.org/anthology/S12-1051.pdf), [STS13](https://www.aclweb.org/anthology/S13-1004/), [STS14](http://alt.qcri.org/semeval2014/task10/), [STS15](http://alt.qcri.org/semeval2015/task2/), [STS16](http://alt.qcri.org/semeval2016/task1/), [STS17](http://alt.qcri.org/semeval2016/task1/), [STS22](https://competitions.codalab.org/competitions/33835), [STSBenchmark](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark)
+* Summarization: [SummEval](https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html)
+
+All these datasets have been preprocessed and can be used for your experiments.

huggingface_dataset/Dataset_Card/nguyenvulebinh_spoken_norm_pattern.md

@@ -0,0 +1,21 @@
+# Vietnamese Inverse Text Normalization 
+
+Inverse text normalization (ITN) is the task that transforms spoken to written styles. It is particularly useful in automatic speech recognition (ASR) systems where proper names are often miss-recognized by their pronunciations instead of the written forms. By applying ITN, we can improve the readability of the ASR system’s output significantly. This dataset provides data for doing ITN task in the Vietnamese language.
+
+For example:
+
+| Spoken (src)                                           | Written (tgt)      | Types                      |
+|--------------------------------------------------|--------------|----------------------------|
+| tám giờ chín phút ngày ba tháng tư năm hai nghìn | 8h9 3/4/2000 | time and date              |
+| tám mét khối năm mươi ki lô gam                  | 8m3 50 kg    | number and unit of measure |
+| không chín sáu hai bảy bảy chín chín không bốn   | 0962779904   | phone number               |
+
+## [Dataset](https://colab.research.google.com/drive/1VlNZfkw_GmAbXiza9LMekMMMRyqTqFl3?usp=sharing)
+
+The ITN dataset has 3 splits: _train_, _validation_, and _test_.
+
+| Dataset Split | Number of Instances in Split |
+| ------------- |----------------------------- |
+| Train         | 500,000                      |
+| Validation    | 2,500                       |
+| Test          | 2,500                       |

huggingface_dataset/Dataset_Card/nli_tr.md

@@ -0,0 +1,281 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- machine-generated
+language:
+- tr
+license:
+- cc-by-3.0
+- cc-by-4.0
+- cc-by-sa-3.0
+- mit
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|snli
+- extended|multi_nli
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+- semantic-similarity-scoring
+- text-scoring
+paperswithcode_id: nli-tr
+pretty_name: Natural Language Inference in Turkish
+configs:
+- multinli_tr
+- snli_tr
+license_details: Open Portion of the American National Corpus
+dataset_info:
+- config_name: snli_tr
+  features:
+  - name: idx
+    dtype: int32
+  - name: premise
+    dtype: string
+  - name: hypothesis
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': entailment
+          '1': neutral
+          '2': contradiction
+  splits:
+  - name: train
+    num_bytes: 71175743
+    num_examples: 550152
+  - name: validation
+    num_bytes: 1359639
+    num_examples: 10000
+  - name: test
+    num_bytes: 1355409
+    num_examples: 10000
+  download_size: 40328942
+  dataset_size: 73890791
+- config_name: multinli_tr
+  features:
+  - name: idx
+    dtype: int32
+  - name: premise
+    dtype: string
+  - name: hypothesis
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': entailment
+          '1': neutral
+          '2': contradiction
+  splits:
+  - name: train
+    num_bytes: 75524150
+    num_examples: 392702
+  - name: validation_matched
+    num_bytes: 1908283
+    num_examples: 10000
+  - name: validation_mismatched
+    num_bytes: 2039392
+    num_examples: 10000
+  download_size: 75518512
+  dataset_size: 79471825
+---
+
+# Dataset Card for "nli_tr"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/boun-tabi/NLI-TR](https://github.com/boun-tabi/NLI-TR)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 110.48 MB
+- **Size of the generated dataset:** 146.26 MB
+- **Total amount of disk used:** 256.74 MB
+
+### Dataset Summary
+
+The Natural Language Inference in Turkish (NLI-TR) is a set of two large scale datasets that were obtained by translating the foundational NLI corpora (SNLI and MNLI) using Amazon Translate.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### multinli_tr
+
+- **Size of downloaded dataset files:** 72.02 MB
+- **Size of the generated dataset:** 75.79 MB
+- **Total amount of disk used:** 147.81 MB
+
+An example of 'validation_matched' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "hypothesis": "Mrinal Sen'in çalışmalarının çoğu Avrupa koleksiyonlarında bulunabilir.",
+    "idx": 7,
+    "label": 1,
+    "premise": "\"Kalküta, sanatsal yaratıcılığa dair herhangi bir iddiaya sahip olan tek diğer üretim merkezi gibi görünüyor, ama ironik bir şek..."
+}
+```
+
+#### snli_tr
+
+- **Size of downloaded dataset files:** 38.46 MB
+- **Size of the generated dataset:** 70.47 MB
+- **Total amount of disk used:** 108.93 MB
+
+An example of 'train' looks as follows.
+```
+{
+    "hypothesis": "Yaşlı bir adam, kızının işten çıkmasını bekçiyken suyunu içer.",
+    "idx": 9,
+    "label": 1,
+    "premise": "Parlak renkli gömlek çalışanları arka planda gülümseme iken yaşlı bir adam bir kahve dükkanında küçük bir masada onun portakal suyu ile oturur."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### multinli_tr
+- `idx`: a `int32` feature.
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+
+#### snli_tr
+- `idx`: a `int32` feature.
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+
+### Data Splits
+
+#### multinli_tr
+
+|           |train |validation_matched|validation_mismatched|
+|-----------|-----:|-----------------:|--------------------:|
+|multinli_tr|392702|             10000|                10000|
+
+#### snli_tr
+
+|       |train |validation|test |
+|-------|-----:|---------:|----:|
+|snli_tr|550152|     10000|10000|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@inproceedings{budur-etal-2020-data,
+    title = "Data and Representation for Turkish Natural Language Inference",
+    author = "Budur, Emrah and
+      "{O}zçelik, Rıza and
+      G"{u}ng"{o}r, Tunga",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    abstract = "Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly.",
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@e-budur](https://github.com/e-budur) for adding this dataset.

huggingface_dataset/Dataset_Card/taln-ls2n_wikinews-fr-100.md

@@ -0,0 +1,64 @@
+---
+annotations_creators:
+- unknown
+language_creators:
+- unknown
+language:
+- fr
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+task_categories:
+- text-mining
+- text-generation
+task_ids:
+- keyphrase-generation
+- keyphrase-extraction
+size_categories:
+- n<1K
+pretty_name: Wikinews-fr-100
+---
+
+# Wikinews-fr-100 Benchmark Dataset for Keyphrase Generation
+
+## About
+
+Wikinews-fr-100 is a dataset for benchmarking keyphrase extraction and generation models.
+The dataset is composed of 100 news articles in French collected from [wikinews](https://fr.wikinews.org/wiki/Accueil).
+Keyphrases were annotated by readers (students in computer science) in an uncontrolled setting (that is, not limited to thesaurus entries).
+Details about the dataset can be found in the original paper [(Bougouin et al., 2013)][bougouin-2013].
+
+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 [(Boudin and Gallina, 2021)][boudin-2021]. Present reference keyphrases are also ordered by their order of apparition in the concatenation of title and abstract.
+
+Text pre-processing (tokenization) is carried out using `spacy` (`fr_core_news_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
+Stemming (Snowball stemmer implementation for french provided in `nltk`) is applied before reference keyphrases are matched against the source text.
+Details about the process can be found in `prmu.py`.
+
+## Content and statistics
+
+The dataset contains the following test split:
+
+| Split      | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
+| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
+| Test       | 100         | 306.9  |  9.64        | 95.91     | 1.40        | 0.85    | 1.84     |
+
+The following data fields are available :
+
+- **id**: unique identifier of the document.
+- **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.
+
+## References
+
+- (Bougouin et al., 2013) Adrien Bougouin, Florian Boudin, and Béatrice Daille. 2013.
+  [TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction][bougouin-2013].
+  In Proceedings of the Sixth International Joint Conference on Natural Language Processing, pages 543–551, Nagoya, Japan. Asian Federation of Natural Language Processing.
+- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
+  [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021]. 
+  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.
+
+[bougouin-2013]: https://aclanthology.org/I13-1062/
+[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/