Upload batch 268 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-e1907042-7494828.md)

huggingface_dataset/Dataset_Card/3ee_regularization-creature.md

@@ -0,0 +1,15 @@
+---
+license: mit
+tags:
+- stable-diffusion
+- regularization-images
+- text-to-image
+- image-to-image
+- dreambooth
+- class-instance
+- preservation-loss-training 
+---
+
+# Creature Regularization Images
+
+A collection of regularization & class instance datasets of creatures for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training.

huggingface_dataset/Dataset_Card/Bonorinoa_kevin_train_12_6.md

@@ -0,0 +1 @@
+Custom Marist QA dataset to train Kevin - version 12/01/22

huggingface_dataset/Dataset_Card/GEM-submissions_lewtun__this-is-a-test-submission-1__1656014763.md

@@ -0,0 +1,12 @@
+---
+benchmark: gem
+type: prediction
+submission_name: This is a test submission 1
+tags:
+- evaluation
+- benchmark
+---
+# GEM Submission
+
+Submission name: This is a test submission 1
+

huggingface_dataset/Dataset_Card/Gazoche_gundam-captioned.md

@@ -0,0 +1,24 @@
+---
+license: cc-by-nc-sa-4.0
+annotations_creators:
+- machine-generated
+language:
+- en
+language_creators:
+- other
+multilinguality:
+- monolingual
+pretty_name: 'Gundam captioned'
+size_categories:
+- n<2K
+tags: []
+task_categories:
+- text-to-image
+task_ids: []
+---
+
+# Dataset Card for captioned Gundam
+
+Scraped from mahq.net (https://www.mahq.net/mecha/gundam/index.htm) and manually cleaned to only keep drawings and "Mobile Suits" (i.e, humanoid-looking machines).
+
+The captions were automatically generated from a generic hardcoded description + the dominant colors as described by [BLIP](https://github.com/salesforce/BLIP).

huggingface_dataset/Dataset_Card/HenryHL_covid19_cases_in_HK_universities.md

@@ -0,0 +1,105 @@
+# COVID19 CASES IN HONG KONG UNIVERSITIES
+## main part: HK Universities COVID19 Cases 
+
+### 🏠 Dashboards
+
+[The Hong Kong Polytechnic University][l1]
+
+[The University of Hong Kong][l2]
+
+[The Chinese University of Hong Kong][l3]
+
+[The Hong Kong Baptist University][l4]
+
+[The City University of Hong Kong][l5]
+
+### 🏠 Info Square
+
+[Public Information Sharing and Personal Ask&Help Square][l6]
+
+
+[l1]: https://datastudio.google.com/reporting/6f62f56f-fd34-4e7b-9ce3-8991fd35ae5e/page/IVVmC
+[l2]: https://datastudio.google.com/reporting/19380e90-a92c-4e22-bbdf-2b4a56b2630a/page/5jWmC
+[l3]: https://datastudio.google.com/reporting/fb0280da-c5c8-4b46-bd29-c80978179536/page/6YXmC
+[l4]: https://datastudio.google.com/reporting/7ad2ae5c-b543-4d8e-94df-f5dd0419b147
+[l5]: https://datastudio.google.com/reporting/b30e540b-3ef6-430c-9abf-94c89c621ade/page/ThbnC
+[l6]: https://docs.google.com/document/d/15zdVq6KPEByHO-xtv6hJh-HQz80t6mUyZ5LMeHjtWOU/edit#heading=h.tv3qxy36yxj8
+
+
+## usage part: Background Description and Lessons Learned
+
+### 🔔 Storyline Start
+
+Back in the February 2022, the Hong Kong was severely impacted by the 5th wave of Covid-19. Each and every day, newspapers published tsunami like and exponentially increasing everywhere confirmed cases. Every citizen felt enormously scared and deeply concerned about uncertainty in the future. Most of people had to work from home or stay at home for entire day, shops were closed at a large scale and almost no one could be seen on any street. Even worse, people are surrounded by unclear and sometimes self-contradictory messages. 
+
+My friend, Pili reminded me that as a soft developer, why not to do something to make the society better. To visualize the data might be a good option and let people see it and calm down. Moreover, good news were that Hong Kong universities started to collect and publish daily cases. As a consequence, the project was launched immediately and benefited on Cloud Native, lightweight, low code and “more important”, zero cost. 
+
+### 🔔 Storyline Continue
+
+Now in the February 2023, the 5th wave of Covid-19 has already faded away. The city and her citizens become resuscitated and invigorated once more. As the government eased Covid-19 regulations and cancelled isolation orders from 30 January 2023, universities no longer collected and updated the data either. The project have to stop here, although so far thousands of visitors have gotten beneficial and expressed gratitude. 
+
+Through this plight and struggle, everyone has learned more or less. As Alexandre Dumas pointed out, “all human wisdom is contained in these two words – WAIT and HOPE.” No matter what happened and will happen, please always take away this two words from the project. WAIT & HOPE.
+
+
+## technic part: System Architecture and Main Technic Analysis
+
+The is a light-weight low-code cloud native project, whose components are built on Google Cloud Platform and Google Workspace.
+
+### ⚙️ Spec
+
+- Database: Google Sheets, Google Docs 
+- Backend: Google Apps Script, GCP Cloud Functions, GCP API Gateway, GCP IAM
+- Frontend: Google Data Studio
+- Language: Javascript
+- Platform: Node.js, NPM
+
+
+## supportive part: Public Information Sources
+### 🔍 Hong Kong Government Sources
+https://www.covidvaccine.gov.hk/pdf/5th_wave_statistics.pdf
+https://www.chp.gov.hk/files/pdf/local_situation_covid19_tc.pdf
+https://www.covidvaccine.gov.hk/pdf/death_analysis.pdf
+https://www.covidvaccine.gov.hk/en/dashboard
+
+### 🔍 The Hong Kong Polytechnic University Sources
+https://www.polyu.edu.hk/cpa/notices/index_student.php
+
+### 🔍 The University of Hong Kong Sources
+https://covid19.hku.hk/control/latest-campus-related-test-positive-cases/
+https://covid19.hku.hk/control/cases-table/
+https://covid19.hku.hk/control/latest-close-contact-with-confirmed-cases/
+
+### 🔍 The Chinese University of Hong Kong Sources
+https://www.cuhk.edu.hk/english/whats-on/faces/confirmed-covid-19-cases.html
+
+### 🔍 The Hong Kong Baptist University Sources
+https://ehsu.hkbu.edu.hk/2019-nCOV/
+
+### 🔍 The City University of Hong Kong Sources
+https://auth.cityu.edu.hk
+
+
+## contributive part: Acknowledgement and Awards
+### 🎉 Thanks 
+#The Hong Kong Government
+
+#The Hong Kong Polytechnic University
+
+#The University of Hong Kong
+
+#The Chinese University of Hong Kong
+
+#The Hong Kong Baptist University
+
+#The City University of Hong Kong
+
+### 🎉 Awards
+Contributors welcome!
+- if you possess more datasets
+- if you want to improve it
+
+### 🎉 Sponsors
+Please contact me! @Henry hengluomail@gmail.com 
+- if you want to access to raw dataset
+- if you want to access to code
+

huggingface_dataset/Dataset_Card/Karavet_pioNER-Armenian-Named-Entity.md

@@ -0,0 +1,55 @@
+---
+language: [hy]
+task_categories: [named-entity-recognition]
+multilinguality: [monolingual]
+task_ids: [named-entity-recognition]
+license: [apache-2.0]
+---
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [pioNER - named entity annotated datasets](#pioNER---named-entity-annotated-datasets)
+  - [Silver-standard dataset](#silver-standard-dataset)
+  - [Gold-standard dataset](#gold-standard-dataset)
+
+
+
+# pioNER - named entity annotated datasets
+
+pioNER corpus provides gold-standard and automatically generated named-entity datasets for the Armenian language.
+
+Alongside the datasets, we release 50-, 100-, 200-, and 300-dimensional GloVe word embeddings trained on a collection of Armenian texts from Wikipedia, news, blogs, and encyclopedia.
+
+## Silver-standard dataset
+
+The generated corpus is automatically extracted and annotated using Armenian Wikipedia. We used a modification of [Nothman et al](https://www.researchgate.net/publication/256660013_Learning_multilingual_named_entity_recognition_from_Wikipedia) and [Sysoev and Andrianov](http://www.dialog-21.ru/media/3433/sysoevaaandrianovia.pdf) approaches to create this corpus. This approach uses links between Wikipedia articles to extract fragments of named-entity annotated texts.
+
+The corpus is split into train and development sets. 
+
+*Table 1. Statistics for pioNER train, development and test sets*
+
+| dataset       | #tokens | #sents | annotation | texts' source |
+|-------------|:--------:|:-----:|:--------:|:-----:|
+| train    | 130719 |  5964  | automatic | Wikipedia |
+| dev | 32528 | 1491 | automatic | Wikipedia |
+| test | 53606 | 2529 | manual | iLur.am |
+
+## Gold-standard dataset
+
+This dataset is a collection of over 250 news articles from iLur.am with manual named-entity annotation. It includes sentences from political, sports, local and world news, and is comparable in size with the test sets of other languages (Table 2). 
+We aim it to serve as a benchmark for future named entity recognition systems designed for the Armenian language.
+
+The dataset contains annotations for 3 popular named entity classes: 
+people (PER), organizations (ORG), and locations (LOC), and is released in CoNLL03 format with IOB tagging scheme. 
+During annotation, we generally relied on categories and [guidelines assembled by BBN](https://catalog.ldc.upenn.edu/docs/LDC2005T33/BBN-Types-Subtypes.html) Technologies for TREC 2002 question answering track
+
+Tokens and sentences were segmented according to the UD standards for the Armenian language from [ArmTreebank project](http://armtreebank.yerevann.com/tokenization/process/).
+
+*Table 2. Comparison of pioNER gold-standard test set with test sets for English, Russian, Spanish and German*
+
+| test dataset       | #tokens | #LOC | #ORG | #PER |
+|-------------|:--------:|:-----:|:--------:|:-----:|
+| Armenian pioNER    | 53606 |  1312  | 1338 | 1274 |
+| Russian factRuEval-2016 | 59382 | 1239 | 1595 | 1353 |
+| German CoNLL03 | 51943 | 1035 | 773 | 1195 |
+| Spanish CoNLL02 | 51533 | 1084 | 1400 | 735 |
+| English CoNLL03 | 46453 | 1668 | 1661 | 1671 |

huggingface_dataset/Dataset_Card/VanessaSchenkel_translation-en-pt.md

@@ -0,0 +1,53 @@
+---
+annotations_creators:
+- found
+language:
+- en
+- pt
+language_creators:
+- found
+license:
+- afl-3.0
+multilinguality:
+- translation
+pretty_name: VanessaSchenkel/translation-en-pt
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+tags: []
+task_categories:
+- translation
+task_ids: []
+---
+How to use it: 
+
+```
+from datasets import load_dataset
+
+remote_dataset = load_dataset("VanessaSchenkel/translation-en-pt", field="data")
+
+remote_dataset
+```
+
+Output:
+```
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'translation'],
+        num_rows: 260482
+    })
+})
+```
+
+Exemple: 
+```
+remote_dataset["train"][5]
+```
+
+Output:
+```
+{'id': '5',
+ 'translation': {'english': 'I have to go to sleep.',
+  'portuguese': 'Tenho de dormir.'}}
+ ```

huggingface_dataset/Dataset_Card/andyyang_stable_diffusion_prompts_2m.md

@@ -0,0 +1,11 @@
+---
+license: cc0-1.0
+---
+# Stable Diffusion Prompts 200m  
+
+Because Diffusion-DB dataset is too big. So I extracted the prompts out for prompt study.   
+
+The file introduction:
+- sd_promts_2m.txt : the main dataset.
+- sd_top5000.keywords.tsv: the top 5000 frequent key words or phrase.
+- 

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-jnlpba-c103d433-1295449602.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- jnlpba
+eval_info:
+  task: entity_extraction
+  model: siddharthtumre/biobert-ner
+  metrics: []
+  dataset_name: jnlpba
+  dataset_config: jnlpba
+  dataset_split: validation
+  col_mapping:
+    tokens: tokens
+    tags: ner_tags
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: siddharthtumre/biobert-ner
+* Dataset: jnlpba
+* Config: jnlpba
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@siddharthtumre](https://huggingface.co/siddharthtumre) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-e1907042-7494828.md

@@ -0,0 +1,31 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- clinc_oos
+eval_info:
+  task: multi_class_classification
+  model: lewtun/roberta-large-finetuned-clinc
+  metrics: []
+  dataset_name: clinc_oos
+  dataset_config: small
+  dataset_split: test
+  col_mapping:
+    text: text
+    target: intent
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Multi-class Text Classification
+* Model: lewtun/roberta-large-finetuned-clinc
+* Dataset: clinc_oos
+
+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/bible_para.md

@@ -0,0 +1,351 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- acu
+- af
+- agr
+- ake
+- am
+- amu
+- ar
+- bg
+- bsn
+- cak
+- ceb
+- ch
+- chq
+- chr
+- cjp
+- cni
+- cop
+- crp
+- cs
+- da
+- de
+- dik
+- dje
+- djk
+- dop
+- ee
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fi
+- fr
+- gbi
+- gd
+- gu
+- gv
+- he
+- hi
+- hr
+- hu
+- hy
+- id
+- is
+- it
+- ja
+- jak
+- jiv
+- kab
+- kbh
+- kek
+- kn
+- ko
+- la
+- lt
+- lv
+- mam
+- mi
+- ml
+- mr
+- my
+- ne
+- nhg
+- nl
+- 'no'
+- ojb
+- pck
+- pes
+- pl
+- plt
+- pot
+- ppk
+- pt
+- quc
+- quw
+- ro
+- rom
+- ru
+- shi
+- sk
+- sl
+- sn
+- so
+- sq
+- sr
+- ss
+- sv
+- syr
+- te
+- th
+- tl
+- tmh
+- tr
+- uk
+- usp
+- vi
+- wal
+- wo
+- xh
+- zh
+- zu
+license:
+- cc0-1.0
+multilinguality:
+- multilingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- translation
+task_ids: []
+paperswithcode_id: null
+pretty_name: BiblePara
+dataset_info:
+- config_name: de-en
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - de
+        - en
+  splits:
+  - name: train
+    num_bytes: 17262178
+    num_examples: 62195
+  download_size: 5440713
+  dataset_size: 17262178
+- config_name: en-fr
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - fr
+  splits:
+  - name: train
+    num_bytes: 17536445
+    num_examples: 62195
+  download_size: 5470044
+  dataset_size: 17536445
+- config_name: en-es
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - es
+  splits:
+  - name: train
+    num_bytes: 17105724
+    num_examples: 62191
+  download_size: 5418998
+  dataset_size: 17105724
+- config_name: en-fi
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - fi
+  splits:
+  - name: train
+    num_bytes: 17486055
+    num_examples: 62026
+  download_size: 5506407
+  dataset_size: 17486055
+- config_name: en-no
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - 'no'
+  splits:
+  - name: train
+    num_bytes: 16681323
+    num_examples: 62107
+  download_size: 5293164
+  dataset_size: 16681323
+- config_name: en-hi
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - hi
+  splits:
+  - name: train
+    num_bytes: 27849361
+    num_examples: 62073
+  download_size: 6224765
+  dataset_size: 27849361
+---
+
+# Dataset Card for BiblePara
+
+## 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:** http://opus.nlpl.eu/bible-uedin.php
+- **Repository:** None
+- **Paper:** https://link.springer.com/article/10.1007/s10579-014-9287-y
+- **Leaderboard:** [More Information Needed]
+- **Point of Contact:** [More Information Needed]
+
+### Dataset Summary
+
+To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
+You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/bible-uedin.php
+E.g.
+
+`dataset = load_dataset("bible_para", lang1="fi", lang2="hi")`
+
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+Here are some examples of questions and facts:
+
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

huggingface_dataset/Dataset_Card/blbooksgenre.md

@@ -0,0 +1,638 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- de
+- en
+- fr
+- nl
+license:
+- cc0-1.0
+multilinguality:
+- multilingual
+size_categories:
+- 10K<n<100K
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+- text-generation
+- fill-mask
+task_ids:
+- topic-classification
+- multi-label-classification
+- language-modeling
+- masked-language-modeling
+pretty_name: British Library Books Genre
+configs:
+- annotated_raw
+- raw
+- title_genre_classifiction
+dataset_info:
+- config_name: title_genre_classifiction
+  features:
+  - name: BL record ID
+    dtype: string
+  - name: title
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': Fiction
+          '1': Non-fiction
+  splits:
+  - name: train
+    num_bytes: 187600
+    num_examples: 1736
+  download_size: 20111420
+  dataset_size: 187600
+- config_name: annotated_raw
+  features:
+  - name: BL record ID
+    dtype: string
+  - name: Name
+    dtype: string
+  - name: Dates associated with name
+    dtype: string
+  - name: Type of name
+    dtype: string
+  - name: Role
+    dtype: string
+  - name: All names
+    sequence: string
+  - name: Title
+    dtype: string
+  - name: Variant titles
+    dtype: string
+  - name: Series title
+    dtype: string
+  - name: Number within series
+    dtype: string
+  - name: Country of publication
+    sequence: string
+  - name: Place of publication
+    sequence: string
+  - name: Publisher
+    dtype: string
+  - name: Date of publication
+    dtype: string
+  - name: Edition
+    dtype: string
+  - name: Physical description
+    dtype: string
+  - name: Dewey classification
+    dtype: string
+  - name: BL shelfmark
+    dtype: string
+  - name: Topics
+    dtype: string
+  - name: Genre
+    dtype: string
+  - name: Languages
+    sequence: string
+  - name: Notes
+    dtype: string
+  - name: BL record ID for physical resource
+    dtype: string
+  - name: classification_id
+    dtype: string
+  - name: user_id
+    dtype: string
+  - name: subject_ids
+    dtype: string
+  - name: annotator_date_pub
+    dtype: string
+  - name: annotator_normalised_date_pub
+    dtype: string
+  - name: annotator_edition_statement
+    dtype: string
+  - name: annotator_FAST_genre_terms
+    dtype: string
+  - name: annotator_FAST_subject_terms
+    dtype: string
+  - name: annotator_comments
+    dtype: string
+  - name: annotator_main_language
+    dtype: string
+  - name: annotator_other_languages_summaries
+    dtype: string
+  - name: annotator_summaries_language
+    dtype: string
+  - name: annotator_translation
+    dtype: string
+  - name: annotator_original_language
+    dtype: string
+  - name: annotator_publisher
+    dtype: string
+  - name: annotator_place_pub
+    dtype: string
+  - name: annotator_country
+    dtype: string
+  - name: annotator_title
+    dtype: string
+  - name: Link to digitised book
+    dtype: string
+  - name: annotated
+    dtype: bool
+  - name: Type of resource
+    dtype:
+      class_label:
+        names:
+          '0': Monograph
+          '1': Serial
+  - name: created_at
+    dtype: timestamp[s]
+  - name: annotator_genre
+    dtype:
+      class_label:
+        names:
+          '0': Fiction
+          '1': Can't tell
+          '2': Non-fiction
+          '3': The book contains both Fiction and Non-Fiction
+  splits:
+  - name: train
+    num_bytes: 3583138
+    num_examples: 4398
+  download_size: 20111420
+  dataset_size: 3583138
+- config_name: raw
+  features:
+  - name: BL record ID
+    dtype: string
+  - name: Name
+    dtype: string
+  - name: Dates associated with name
+    dtype: string
+  - name: Type of name
+    dtype: string
+  - name: Role
+    dtype: string
+  - name: All names
+    sequence: string
+  - name: Title
+    dtype: string
+  - name: Variant titles
+    dtype: string
+  - name: Series title
+    dtype: string
+  - name: Number within series
+    dtype: string
+  - name: Country of publication
+    sequence: string
+  - name: Place of publication
+    sequence: string
+  - name: Publisher
+    dtype: string
+  - name: Date of publication
+    dtype: string
+  - name: Edition
+    dtype: string
+  - name: Physical description
+    dtype: string
+  - name: Dewey classification
+    dtype: string
+  - name: BL shelfmark
+    dtype: string
+  - name: Topics
+    dtype: string
+  - name: Genre
+    dtype: string
+  - name: Languages
+    sequence: string
+  - name: Notes
+    dtype: string
+  - name: BL record ID for physical resource
+    dtype: string
+  - name: classification_id
+    dtype: string
+  - name: user_id
+    dtype: string
+  - name: subject_ids
+    dtype: string
+  - name: annotator_date_pub
+    dtype: string
+  - name: annotator_normalised_date_pub
+    dtype: string
+  - name: annotator_edition_statement
+    dtype: string
+  - name: annotator_FAST_genre_terms
+    dtype: string
+  - name: annotator_FAST_subject_terms
+    dtype: string
+  - name: annotator_comments
+    dtype: string
+  - name: annotator_main_language
+    dtype: string
+  - name: annotator_other_languages_summaries
+    dtype: string
+  - name: annotator_summaries_language
+    dtype: string
+  - name: annotator_translation
+    dtype: string
+  - name: annotator_original_language
+    dtype: string
+  - name: annotator_publisher
+    dtype: string
+  - name: annotator_place_pub
+    dtype: string
+  - name: annotator_country
+    dtype: string
+  - name: annotator_title
+    dtype: string
+  - name: Link to digitised book
+    dtype: string
+  - name: annotated
+    dtype: bool
+  - name: Type of resource
+    dtype:
+      class_label:
+        names:
+          '0': Monograph
+          '1': Serial
+          '2': Monographic component part
+  - name: created_at
+    dtype: string
+  - name: annotator_genre
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 27518816
+    num_examples: 55343
+  download_size: 20111420
+  dataset_size: 27518816
+---
+
+# Dataset Card for blbooksgenre
+
+## Table of Contents
+
+- [Dataset Card for blbooksgenre](#dataset-card-for-blbooksgenre)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+      - [Supervised tasks](#supervised-tasks)
+  - [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)
+      - [Colonialism](#colonialism)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**: [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312)
+- **Repository:** [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312)
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This dataset consists of metadata relating to books [digitised by the British Library in partnership with Microsoft](https://www.bl.uk/collection-guides/google-books-digitised-printed-heritage). Some of this metadata was exported from the British Library catalogue whilst others was generated as part of a crowdsourcing project. The text of this book and other metadata can be found on the [date.bl](https://data.bl.uk/bl_labs_datasets/#3) website.
+
+The majority of the books in this collection were published in the 18th and 19th Century but the collection also includes a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas including geography, philosophy, history, poetry and literature and are published in a variety of languages.
+
+For the subsection of the data which contains additional crowsourced annotations the date of publication breakdown is as follows:
+
+|      | Date of publication |
+| ---- | ------------------- |
+| 1630 | 8                   |
+| 1690 | 4                   |
+| 1760 | 10                  |
+| 1770 | 5                   |
+| 1780 | 5                   |
+| 1790 | 18                  |
+| 1800 | 45                  |
+| 1810 | 96                  |
+| 1820 | 152                 |
+| 1830 | 182                 |
+| 1840 | 259                 |
+| 1850 | 400                 |
+| 1860 | 377                 |
+| 1870 | 548                 |
+| 1880 | 776                 |
+| 1890 | 1484                |
+| 1900 | 17                  |
+| 1910 | 1                   |
+| 1970 | 1                   |
+
+[More Information Needed]
+
+
+### Supported Tasks and Leaderboards
+
+The digitised books collection which this dataset describes has been used in a variety of digital history and humanities projects since being published.
+
+This dataset is suitable for a variety of unsupervised tasks and for a 'genre classification task'.
+
+#### Supervised tasks
+
+The main possible use case for this dataset is to develop and evaluate 'genre classification' models. The dataset includes human generated labels for whether a book is 'fiction' or 'non-fiction'. This has been used to train models for genre classifcation which predict whether a book is 'fiction' or 'non-fiction' based on its title. 
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+The dataset currently has three configurations intended to support a range of tasks for which this dataset could be used for:
+
+- `title_genre_classifiction` : this creates a de-duplicated version of the dataset with the `BL record`, `title` and `label`.
+- `annotated_raw`:  This version of the dataset includes all fields from the original dataset which are annotated.  This includes duplication from different annotators"
+- `raw`: This version of the dataset includes all the data from the original data including data without annotations. 
+
+### Data Instances
+
+An example data instance from the `title_genre_classifiction` config:
+
+```python
+{'BL record ID': '014603046',
+ 'title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
+ 'label': 0}
+```
+
+An example data instance from the `annotated_raw` config:
+
+```python
+{'BL record ID': '014603046',
+ 'Name': 'Yates, William Joseph H.',
+ 'Dates associated with name': '',
+ 'Type of name': 'person',
+ 'Role': '',
+ 'All names': ['Yates, William Joseph H. [person] ', ' Y, W. J. H. [person]'],
+ 'Title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
+ 'Variant titles': '',
+ 'Series title': '',
+ 'Number within series': '',
+ 'Country of publication': ['England'],
+ 'Place of publication': ['London'],
+ 'Publisher': '',
+ 'Date of publication': '1879',
+ 'Edition': '',
+ 'Physical description': 'pages not numbered, 21 cm',
+ 'Dewey classification': '',
+ 'BL shelfmark': 'Digital Store 11601.f.36. (1.)',
+ 'Topics': '',
+ 'Genre': '',
+ 'Languages': ['English'],
+ 'Notes': 'In verse',
+ 'BL record ID for physical resource': '004079262',
+ 'classification_id': '267476823.0',
+ 'user_id': '15.0',
+ 'subject_ids': '44369003.0',
+ 'annotator_date_pub': '1879',
+ 'annotator_normalised_date_pub': '1879',
+ 'annotator_edition_statement': 'NONE',
+ 'annotator_FAST_genre_terms': '655 7 ‡aPoetry‡2fast‡0(OCoLC)fst01423828',
+ 'annotator_FAST_subject_terms': '60007 ‡aAlice,‡cGrand Duchess, consort of Ludwig IV, Grand Duke of Hesse-Darmstadt,‡d1843-1878‡2fast‡0(OCoLC)fst00093827',
+ 'annotator_comments': '',
+ 'annotator_main_language': '',
+ 'annotator_other_languages_summaries': 'No',
+ 'annotator_summaries_language': '',
+ 'annotator_translation': 'No',
+ 'annotator_original_language': '',
+ 'annotator_publisher': 'NONE',
+ 'annotator_place_pub': 'London',
+ 'annotator_country': 'enk',
+ 'annotator_title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
+ 'Link to digitised book': 'http://access.bl.uk/item/viewer/ark:/81055/vdc_00000002842E',
+ 'annotated': True,
+ 'Type of resource': 0,
+ 'created_at': datetime.datetime(2020, 8, 11, 14, 30, 33),
+ 'annotator_genre': 0}
+```
+
+### Data Fields
+
+The data fields differ slightly between configs. All possible fields for the `annotated_raw` config are listed below. For the `raw` version of the dataset datatypes are usually string to avoid errors when processing missing values.
+
+- `BL record ID`: an internal ID used by the British Library, this can be useful for linking this data to other BL collections.
+- `Name`: name associated with the item (usually author)
+- `Dates associated with name`: dates associated with above e.g. DOB
+- `Type of name`: whether `Name` is a person or an organization etc. 
+- `Role`: i.e. whether `Name` is `author`, `publisher` etc.
+- `All names`: a fuller list of names associated with the item.
+- `Title`: The title of the work
+- `Variant titles`
+- `Series title`
+- `Number within series`
+- `Country of publication`: encoded as a list of countries listed in the metadata
+- `Place of publication`:  encoded as a list of places listed in the metadata 
+- `Publisher`
+- `Date of publication`: this is encoded as a string since this field can include data ranges i.e.`1850-1855`. 
+- `Edition`
+- `Physical description`: encoded as a string since the format of this field varies
+- `Dewey classification`
+- `BL shelfmark`: a British Library shelf mark
+- `Topics`: topics included in the catalogue record
+- `Genre` the genre information included in the original catalogue record note that this is often missing 
+- `Languages`; encoded as a list of languages 
+- `Notes`: notes from the catalogue record
+- `BL record ID for physical resource`
+
+The following fields are all generated via the crowdsourcing task (discussed in more detail below)
+
+- `classification_id`: ID for the classification in the annotation task
+- `user_id` ID for the annotator
+- `subject_ids`: internal annotation task ID
+- `annotator_date_pub`: an updated publication data
+- `annotator_normalised_date_pub`: normalized version of the above
+- `annotator_edition_statement` updated edition 
+- `annotator_FAST_genre_terms`: [FAST classification genre terms](https://www.oclc.org/research/areas/data-science/fast.html)
+- `annotator_FAST_subject_terms`: [FAST subject terms](https://www.oclc.org/research/areas/data-science/fast.html)
+- `annotator_comments`: free form comments 
+- `annotator_main_language`
+- `annotator_other_languages_summaries` 
+- `'annotator_summaries_language`
+- `annotator_translation`
+- `annotator_original_language`
+- `annotator_publisher`
+- `annotator_place_pub`
+- `annotator_country`
+- `annotator_title`
+- `Link to digitised book`
+- `annotated`: `bool` flag to indicate if row has annotations or not
+- `created_at`: when the annotation was created
+- `annotator_genre`: the updated annotation for the `genre` of the book.
+
+Finally the `label` field of the `title_genre_classifiction` configuration  is a class label with values 0 (Fiction) or 1 (Non-fiction).
+
+[More Information Needed]
+
+### Data Splits
+
+This dataset contains a single split `train`.
+
+## Dataset Creation
+
+**Note** this section is a work in progress.
+
+### Curation Rationale
+
+The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization) i.e. projects where there is a goal to quickly digitise large volumes of materials shape the selection of materials to include in a number of ways. Some consideratoins which are often involved in the decision of whether to include items for digitization include (but are not limited to):
+
+- copyright status
+- preservation needs- the size of an item, very large and very small items are often hard to digitize quickly
+
+These criteria can have knock-on effects on the makeup of a collection. For example systematically excluding large books may result in some types of book content not being digitized. Large volumes are likely to be correlated to content to at least some extent so excluding them from digitization will mean that material is under represented. Similarly copyright status is often (but not only) determined by publication data. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date.
+
+All of the above is largely to make clear that this collection was not curated with the aim of creating a representative sample of the British Library's holdings. Some material will be over-represented and other under-represented. Similarly, the collection should not be considered a representative sample of what was published across the time period covered by the dataset (nor that that the relative proportions of the data for each time period represent a proportional sample of publications from that period). 
+
+[More Information Needed]
+
+### Source Data
+
+The original source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. "Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It's existed in English law since 1662."[source](https://www.bl.uk/legal-deposit/about-legal-deposit).
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+This version of the dataset was created partially from data exported from British Library catalogue records and partially via data generated from a crowdsourcing task involving British Library staff.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+The data does includes metadata associated with the books these are produced by British Library staff. The additional annotations were carried out during 2020 as part of an internal crowdsourcing task.
+
+#### Annotation process
+
+New annotations were produced via a crowdsourcing tasks. Annotators have the option to pick titles from a particular language subset from the broader digitized 19th century books collection. As a result the annotations are not random and overrepresent some languages.
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+Staff working at the British Library. Most of these staff work with metadata as part of their jobs and so could be considered expert annotators. 
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+There a range of considerations around using the data. These include the representativeness of the dataset, the bias towards particular languages etc. 
+
+It is also important to note that library metadata is not static. The metadata held in library catalogues is updated and changed over time for a variety of reasons. 
+
+The way in which different institutions catalogue items also varies. As a result it is important to evaluate the performance of any models trained on this data before applying to a new collection.
+
+[More Information Needed]
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+The text in this collection is derived from historic text. As a result the text will reflect to social beliefs and attitudes of this time period. The titles of the book give some sense of their content. Examples of book titles which appear in the data (these are randomly sampled from all titles):
+
+- 'Rhymes and Dreams, Legends of Pendle Forest, and other poems',
+ - "Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General's Department, Horse Guards, War Office, etc",
+- 'The fan. A poem',
+- 'Grif; a story of Australian Life',
+- 'Calypso; a masque: in three acts, etc', 
+- 'Tales Uncle told [With illustrative woodcuts.]',
+- 'Questings',
+- 'Home Life on an Ostrich Farm. With ... illustrations',
+- 'Bulgarya i Bulgarowie', 
+- 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc',
+- 'The Corsair, a tale',
+  'Poems ... With notes [With a portrait.]',
+- 'Report of the Librarian for the year 1898 (1899, 1901, 1909)',
+- "The World of Thought. A novel. By the author of 'Before I began to speak.'",
+- 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc']
+
+Whilst using titles alone, is obviously insufficient to integrate bias in this collection it gives some insight into the topics covered by books in the corpus. Further looking into the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list.
+
+#### Colonialism
+
+We can see even in the above random sample of titles examples of colonial attitudes. We can try and interrogate this further by searching for the name of countries which were part of the British Empire at the time many of these books were published.
+
+Searching for the string `India` in the titles and randomly sampling 10 titles returns:
+
+- "Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the 'Calcutta Weekly Englishman.'",
+- 'A Winter in India and Malaysia among the Methodist Missions',
+- "The Tourist's Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition",
+- 'Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson',
+- "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]",
+- 'The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies',
+- "From Tonkin to India : by the sources of the Irawadi, January '95-January '96",
+- 'Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844',
+- 'The Andaman Islands; their colonization, etc. A correspondence addressed to the India Office',
+- 'Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle']
+
+Searching form the string `Africa` in the titles and randomly sampling 10 titles returns:
+
+- ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada',
+- 'To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]',
+- 'Diamonds and Gold in South Africa ... With maps, etc',
+- 'Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition',
+- 'A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts',
+- 'Side Lights on South Africa ... With a map, etc',
+- 'My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc',
+- 'Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations',
+- '[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder',
+- 'Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc']
+
+Whilst this dataset doesn't include the underlying text it is important to consider the potential attitudes represented in the title of the books, or the full text if you are using this dataset in conjunction with the full text.
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license.
+
+### Citation Information
+
+```bibtex
+@misc{british library_genre, 
+title={ 19th Century Books - metadata with additional crowdsourced annotations}, 
+url={https://doi.org/10.23636/BKHQ-0312},
+author={{British Library} and  Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annelies and Wollner, Ildi},
+year={2021}}
+```
+
+### Contributions
+
+Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.

huggingface_dataset/Dataset_Card/diwank_hinglish-dump.md

@@ -0,0 +1,28 @@
+---
+license: mit
+---
+
+# Hinglish Dump
+
+Raw merged dump of Hinglish (hi-EN) datasets.
+
+## Subsets and features
+
+Subsets:
+- crowd_transliteration 
+- hindi_romanized_dump 
+- hindi_xlit 
+- hinge 
+- hinglish_norm 
+- news2018
+
+```
+_FEATURE_NAMES = [
+    "target_hinglish",
+    "source_hindi",
+    "parallel_english",
+    "annotations",
+    "raw_input",
+    "alternates",
+]
+```

huggingface_dataset/Dataset_Card/flax-community_swahili-safi.md

@@ -0,0 +1,28 @@
+# Swahili-Safi Dataset
+
+A relatively clean dataset for Swahili language modeling, built by combining and cleaning several existing datasets.
+
+Sources include:
+```
+mc4-sw
+oscar-sw
+swahili_news
+IWSLT
+XNLI
+flores 101
+swahili-lm
+gamayun-swahili-minikit
+broadcastnews-sw
+subset of wikipedia-en translated (using m2m100) to sw
+```
+
+In total this dataset is ~3.5 GB in size with over 21 million lines of text.
+
+## Usage
+
+This dataset can be downloaded and used as follows:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("flax-community/swahili-safi")
+```

huggingface_dataset/Dataset_Card/income_trec-news-top-20-gen-queries.md

@@ -0,0 +1,510 @@
+---
+annotations_creators: []
+language_creators: []
+language:
+- en
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+paperswithcode_id: beir
+pretty_name: BEIR Benchmark
+size_categories:
+  msmarco:
+  - 1M<n<10M
+  trec-covid:
+  - 100k<n<1M
+  nfcorpus:
+  - 1K<n<10K
+  nq:
+  - 1M<n<10M
+  hotpotqa:
+  - 1M<n<10M
+  fiqa:
+  - 10K<n<100K
+  arguana:
+  - 1K<n<10K
+  touche-2020:
+  - 100K<n<1M
+  cqadupstack:
+  - 100K<n<1M
+  quora:
+  - 100K<n<1M
+  dbpedia:
+  - 1M<n<10M
+  scidocs:
+  - 10K<n<100K
+  fever:
+  - 1M<n<10M
+  climate-fever:
+  - 1M<n<10M
+  scifact:
+  - 1K<n<10K
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# NFCorpus: 20 generated queries (BEIR Benchmark)
+
+This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
+
+- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
+- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
+- Questions generated: 20
+- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
+
+
+Below contains the old dataset card for the BEIR benchmark.
+
+
+# Dataset Card for BEIR Benchmark
+
+## 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/UKPLab/beir
+- **Repository:** https://github.com/UKPLab/beir
+- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
+- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
+- **Point of Contact:** nandan.thakur@uwaterloo.ca
+
+### Dataset Summary
+
+BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
+
+- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
+- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
+- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
+- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
+- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
+- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
+- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
+- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
+- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
+
+All these datasets have been preprocessed and can be used for your experiments.
+
+
+```python
+
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
+
+The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
+
+### Languages
+
+All tasks are in English (`en`).
+
+## Dataset Structure
+
+All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
+- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
+- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
+- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
+
+### Data Instances
+
+A high level example of any beir dataset:
+
+```python
+corpus = {
+    "doc1" : {
+        "title": "Albert Einstein", 
+        "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
+                 one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
+                 its influence on the philosophy of science. He is best known to the general public for his mass–energy \
+                 equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
+                 Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
+                 of the photoelectric effect', a pivotal step in the development of quantum theory."
+        },
+    "doc2" : {
+        "title": "", # Keep title an empty string if not present
+        "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
+                 malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
+                 with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
+    },
+}
+
+queries = {
+    "q1" : "Who developed the mass-energy equivalence formula?",
+    "q2" : "Which beer is brewed with a large proportion of wheat?"
+}
+
+qrels = {
+    "q1" : {"doc1": 1},
+    "q2" : {"doc2": 1},
+}
+```
+
+### Data Fields
+
+Examples from all configurations have the following features:
+
+### Corpus
+- `corpus`: a `dict` feature representing the document title and passage text, made up of:
+    - `_id`: a `string` feature representing the unique document id
+        - `title`: a `string` feature, denoting the title of the document.
+        - `text`: a `string` feature, denoting the text of the document.
+
+### Queries
+- `queries`: a `dict` feature representing the query, made up of:
+    - `_id`: a `string` feature representing the unique query id
+    - `text`: a `string` feature, denoting the text of the query.
+
+### Qrels
+- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
+    - `_id`: a `string` feature representing the query id
+        - `_id`: a `string` feature, denoting the document id.
+        - `score`: a `int32` feature, denoting the relevance judgement between query and document.
+
+
+### Data Splits
+
+| Dataset   | Website| BEIR-Name | Type | Queries  | Corpus | Rel D/Q | Down-load | md5 |
+| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
+| MSMARCO    | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``|  6,980   |  8.84M     |    1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
+| TREC-COVID |  [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50|  171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
+| NFCorpus   | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``|  323     |  3.6K     |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
+| BioASQ     | [Homepage](http://bioasq.org) | ``bioasq``|  ``train``<br>``test`` | 500    |  14.91M    |  8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
+| NQ         | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452   |  2.68M  |  1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
+| HotpotQA   | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``|  7,405   |  5.23M  |  2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip)  | ``f412724f78b0d91183a0e86805e16114`` |
+| FiQA-2018  | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``|  648     |  57K    |  2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip)  | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
+| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97   |  2.86M  |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
+| TREC-NEWS  | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news``    | ``test``| 57    |  595K    |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
+| ArguAna    | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406     |  8.67K    |  1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip)  | ``8ad3e3c2a5867cdced806d6503f29b99`` |
+| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49     |  382K    |  19.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
+| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 |  457K  |  1.4 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
+| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000     |  523K    |  1.6 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
+| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400    |  4.63M    |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
+| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000     |  25K    |  4.9 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
+| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``|  6,666     |  5.42M    |  1.2|  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip)  | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
+| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``|  1,535     |  5.42M |  3.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip)  | ``8b66f0a9126c521bae2bde127b4dc99d`` |
+| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``|  300     |  5K    |  1.1 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip)  | ``5f7d1de60b170fc8027bb7898e2efca1`` |
+| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249  |  528K  |  69.9 |  No  |  [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04)  |
+
+
+## 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
+
+Cite as:
+```
+@inproceedings{
+thakur2021beir,
+title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
+author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
+booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+year={2021},
+url={https://openreview.net/forum?id=wCu6T5xFjeJ}
+}
+```
+
+### Contributions
+
+Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus 
+
+
+# Dataset Card for BEIR Benchmark
+
+## 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/UKPLab/beir
+- **Repository:** https://github.com/UKPLab/beir
+- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
+- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
+- **Point of Contact:** nandan.thakur@uwaterloo.ca
+
+### Dataset Summary
+
+BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
+
+- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
+- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
+- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
+- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
+- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
+- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
+- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
+- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
+- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
+
+All these datasets have been preprocessed and can be used for your experiments.
+
+
+```python
+
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
+
+The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
+
+### Languages
+
+All tasks are in English (`en`).
+
+## Dataset Structure
+
+All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
+- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
+- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
+- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
+
+### Data Instances
+
+A high level example of any beir dataset:
+
+```python
+corpus = {
+    "doc1" : {
+        "title": "Albert Einstein", 
+        "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
+                 one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
+                 its influence on the philosophy of science. He is best known to the general public for his mass–energy \
+                 equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
+                 Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
+                 of the photoelectric effect', a pivotal step in the development of quantum theory."
+        },
+    "doc2" : {
+        "title": "", # Keep title an empty string if not present
+        "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
+                 malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
+                 with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
+    },
+}
+
+queries = {
+    "q1" : "Who developed the mass-energy equivalence formula?",
+    "q2" : "Which beer is brewed with a large proportion of wheat?"
+}
+
+qrels = {
+    "q1" : {"doc1": 1},
+    "q2" : {"doc2": 1},
+}
+```
+
+### Data Fields
+
+Examples from all configurations have the following features:
+
+### Corpus
+- `corpus`: a `dict` feature representing the document title and passage text, made up of:
+    - `_id`: a `string` feature representing the unique document id
+        - `title`: a `string` feature, denoting the title of the document.
+        - `text`: a `string` feature, denoting the text of the document.
+
+### Queries
+- `queries`: a `dict` feature representing the query, made up of:
+    - `_id`: a `string` feature representing the unique query id
+    - `text`: a `string` feature, denoting the text of the query.
+
+### Qrels
+- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
+    - `_id`: a `string` feature representing the query id
+        - `_id`: a `string` feature, denoting the document id.
+        - `score`: a `int32` feature, denoting the relevance judgement between query and document.
+
+
+### Data Splits
+
+| Dataset   | Website| BEIR-Name | Type | Queries  | Corpus | Rel D/Q | Down-load | md5 |
+| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
+| MSMARCO    | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``|  6,980   |  8.84M     |    1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
+| TREC-COVID |  [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50|  171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
+| NFCorpus   | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``|  323     |  3.6K     |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
+| BioASQ     | [Homepage](http://bioasq.org) | ``bioasq``|  ``train``<br>``test`` | 500    |  14.91M    |  8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
+| NQ         | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452   |  2.68M  |  1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
+| HotpotQA   | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``|  7,405   |  5.23M  |  2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip)  | ``f412724f78b0d91183a0e86805e16114`` |
+| FiQA-2018  | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``|  648     |  57K    |  2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip)  | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
+| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97   |  2.86M  |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
+| TREC-NEWS  | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news``    | ``test``| 57    |  595K    |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
+| ArguAna    | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406     |  8.67K    |  1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip)  | ``8ad3e3c2a5867cdced806d6503f29b99`` |
+| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49     |  382K    |  19.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
+| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 |  457K  |  1.4 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
+| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000     |  523K    |  1.6 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
+| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400    |  4.63M    |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
+| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000     |  25K    |  4.9 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
+| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``|  6,666     |  5.42M    |  1.2|  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip)  | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
+| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``|  1,535     |  5.42M |  3.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip)  | ``8b66f0a9126c521bae2bde127b4dc99d`` |
+| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``|  300     |  5K    |  1.1 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip)  | ``5f7d1de60b170fc8027bb7898e2efca1`` |
+| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249  |  528K  |  69.9 |  No  |  [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04)  |
+
+
+## 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
+
+Cite as:
+```
+@inproceedings{
+thakur2021beir,
+title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
+author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
+booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+year={2021},
+url={https://openreview.net/forum?id=wCu6T5xFjeJ}
+}
+```
+
+### Contributions
+
+Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

huggingface_dataset/Dataset_Card/keremberke_clash-of-clans-object-detection.md

@@ -0,0 +1,81 @@
+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+- roboflow2huggingface
+- Gaming
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/clash-of-clans-object-detection" src="https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['ad', 'airsweeper', 'bombtower', 'canon', 'clancastle', 'eagle', 'inferno', 'kingpad', 'mortar', 'queenpad', 'rcpad', 'scattershot', 'th13', 'wardenpad', 'wizztower', 'xbow']
+```
+
+
+### Number of Images
+
+```json
+{'train': 88, 'test': 13, 'valid': 24}
+```
+
+
+### How to Use
+
+- Install [datasets](https://pypi.org/project/datasets/):
+
+```bash
+pip install datasets
+```
+
+- Load the dataset:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("keremberke/clash-of-clans-object-detection", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y/dataset/5](https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y/dataset/5?ref=roboflow2huggingface?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ clash-of-clans-vop4y_dataset,
+    title = { Clash of Clans Dataset },
+    type = { Open Source Dataset },
+    author = { Find This Base },
+    howpublished = { \\url{ https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y } },
+    url = { https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { feb },
+    note = { visited on 2023-01-18 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.ai on March 30, 2022 at 4:31 PM GMT
+
+It includes 125 images.
+CoC are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 1920x1920 (Fit (black edges))
+
+No image augmentation techniques were applied.
+
+
+

huggingface_dataset/Dataset_Card/keremberke_indoor-scene-classification.md

@@ -0,0 +1,80 @@
+---
+task_categories:
+- image-classification
+tags:
+- roboflow
+- roboflow2huggingface
+- Retail
+- Pest Control
+- Benchmark
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/indoor-scene-classification" src="https://huggingface.co/datasets/keremberke/indoor-scene-classification/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['meeting_room', 'cloister', 'stairscase', 'restaurant', 'hairsalon', 'children_room', 'dining_room', 'lobby', 'museum', 'laundromat', 'computerroom', 'grocerystore', 'hospitalroom', 'buffet', 'office', 'warehouse', 'garage', 'bookstore', 'florist', 'locker_room', 'inside_bus', 'subway', 'fastfood_restaurant', 'auditorium', 'studiomusic', 'airport_inside', 'pantry', 'restaurant_kitchen', 'casino', 'movietheater', 'kitchen', 'waitingroom', 'artstudio', 'toystore', 'kindergarden', 'trainstation', 'bedroom', 'mall', 'corridor', 'bar', 'classroom', 'shoeshop', 'dentaloffice', 'videostore', 'laboratorywet', 'tv_studio', 'church_inside', 'operating_room', 'jewelleryshop', 'bathroom', 'clothingstore', 'closet', 'winecellar', 'livingroom', 'nursery', 'gameroom', 'inside_subway', 'deli', 'bakery', 'library', 'prisoncell', 'gym', 'concert_hall', 'greenhouse', 'elevator', 'poolinside', 'bowling']
+```
+
+
+### Number of Images
+
+```json
+{'train': 10885, 'test': 1558, 'valid': 3128}
+```
+
+
+### How to Use
+
+- Install [datasets](https://pypi.org/project/datasets/):
+
+```bash
+pip install datasets
+```
+
+- Load the dataset:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("keremberke/indoor-scene-classification", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/dataset/5](https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/dataset/5?ref=roboflow2huggingface)
+
+### Citation
+
+```
+
+```
+
+### License
+MIT
+
+### Dataset Summary
+This dataset was exported via roboflow.com on October 24, 2022 at 4:09 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 15571 images.
+Indoor-scenes are annotated in folder format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 416x416 (Stretch)
+
+No image augmentation techniques were applied.
+
+
+

huggingface_dataset/Dataset_Card/sayakpaul_nyu_depth_v2.md

@@ -0,0 +1,246 @@
+---
+license: apache-2.0
+language:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- depth-estimation
+task_ids: []
+pretty_name: NYU Depth V2
+tags: 
+- depth-estimation
+paperswithcode_id: nyuv2
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: depth_map
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 20212097551
+    num_examples: 47584
+  - name: validation
+    num_bytes: 240785762
+    num_examples: 654
+  download_size: 35151124480
+  dataset_size: 20452883313
+---
+
+# Dataset Card for NYU Depth V2
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Visualization](#visualization)
+- [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:** [NYU Depth Dataset V2 homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
+- **Repository:** Fast Depth [repository](https://github.com/dwofk/fast-depth) which was used to source the dataset in this repository. It is a preprocessed version of the original NYU Depth V2 dataset linked above. It is also used in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/nyu_depth_v2).
+- **Papers:** [Indoor Segmentation and Support Inference from RGBD Images](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) and [FastDepth: Fast Monocular Depth Estimation on Embedded Systems](https://arxiv.org/abs/1903.03273)
+- **Point of Contact:** [Nathan Silberman](mailto:silberman@@cs.nyu.edu) and [Diana Wofk](mailto:dwofk@alum.mit.edu)
+
+### Dataset Summary
+
+As per the [dataset homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html):
+
+The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft [Kinect](http://www.xbox.com/kinect). It features:
+
+* 1449 densely labeled pairs of aligned RGB and depth images
+* 464 new scenes taken from 3 cities
+* 407,024 new unlabeled frames
+* Each object is labeled with a class and an instance number (cup1, cup2, cup3, etc)
+
+The dataset has several components:
+
+* Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels.
+* Raw: The raw rgb, depth and accelerometer data as provided by the Kinect.
+* Toolbox: Useful functions for manipulating the data and labels.
+
+### Supported Tasks
+
+- `depth-estimation`: Depth estimation is the task of approximating the perceived depth of a given image. In other words, it's about measuring the distance of each image pixel from the camera.
+- `semantic-segmentation`: Semantic segmentation is the task of associating every pixel of an image to a class label. 
+
+There are other tasks supported by this dataset as well. You can find more about them by referring to [this resource](https://paperswithcode.com/dataset/nyuv2).
+
+
+### Languages
+
+English.
+
+## Dataset Structure
+
+### Data Instances
+
+A data point comprises an image and its annotation depth map for both the `train` and `validation` splits. 
+
+```
+{
+  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB at 0x1FF32A3EDA0>,
+  'depth_map': <PIL.PngImagePlugin.PngImageFile image mode=L at 0x1FF32E5B978>,
+}
+```
+
+### Data Fields
+
+- `image`: A `PIL.Image.Image` object containing the image. 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]`.
+- `depth_map`: A `PIL.Image.Image` object containing the annotation depth map.
+
+### Data Splits
+
+The data is split into training, and validation splits. The training data contains 47584 images, and the validation data contains 654 images.
+
+## Visualization
+
+You can use the following code snippet to visualize samples from the dataset:
+
+```py
+from datasets import load_dataset
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+cmap = plt.cm.viridis
+
+ds = load_dataset("sayakpaul/nyu_depth_v2")
+
+
+def colored_depthmap(depth, d_min=None, d_max=None):
+    if d_min is None:
+        d_min = np.min(depth)
+    if d_max is None:
+        d_max = np.max(depth)
+    depth_relative = (depth - d_min) / (d_max - d_min)
+    return 255 * cmap(depth_relative)[:,:,:3] # H, W, C
+
+
+def merge_into_row(input, depth_target):
+    input = np.array(input)
+    depth_target = np.squeeze(np.array(depth_target))
+
+    d_min = np.min(depth_target)
+    d_max = np.max(depth_target)
+    depth_target_col = colored_depthmap(depth_target, d_min, d_max)
+    img_merge = np.hstack([input, depth_target_col])
+    
+    return img_merge
+
+
+random_indices = np.random.choice(len(ds["train"]), 9).tolist()
+train_set = ds["train"]
+
+plt.figure(figsize=(15, 6))
+
+for i, idx in enumerate(random_indices):
+    ax = plt.subplot(3, 3, i + 1)
+    image_viz = merge_into_row(
+        train_set[idx]["image"], train_set[idx]["depth_map"]
+    )
+    plt.imshow(image_viz.astype("uint8"))
+    plt.axis("off")
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+The rationale from [the paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) that introduced the NYU Depth V2 dataset:
+
+> We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation.
+
+### Source Data
+
+#### Initial Data Collection 
+
+> The dataset consists of 1449 RGBD images, gathered from a wide range
+of commercial and residential buildings in three different US cities, comprising
+464 different indoor scenes across 26 scene classes.A dense per-pixel labeling was
+obtained for each image using Amazon Mechanical Turk.
+
+### Annotations
+
+#### Annotation process
+
+This is an involved process. Interested readers are referred to Sections 2, 3, and 4 of the [original paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf). 
+
+#### Who are the annotators?
+
+AMT annotators.
+
+### 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
+
+* Original NYU Depth V2 dataset: Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus
+* Preprocessed version: Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze
+
+### Licensing Information
+
+The preprocessed NYU Depth V2 dataset is licensed under a [MIT License](https://github.com/dwofk/fast-depth/blob/master/LICENSE).
+
+### Citation Information
+
+```bibtex
+@inproceedings{Silberman:ECCV12,
+  author    = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus},
+  title     = {Indoor Segmentation and Support Inference from RGBD Images},
+  booktitle = {ECCV},
+  year      = {2012}
+}
+
+@inproceedings{icra_2019_fastdepth,
+	author      = {{Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne}},
+	title       = {{FastDepth: Fast Monocular Depth Estimation on Embedded Systems}},
+	booktitle   = {{IEEE International Conference on Robotics and Automation (ICRA)}},
+	year        = {{2019}}
+}
+```
+
+### Contributions
+
+Thanks to [@sayakpaul](https://huggingface.co/sayakpaul) for adding this dataset.

huggingface_dataset/Dataset_Card/stas_cm4-synthetic-testing.md

@@ -0,0 +1,14 @@
+---
+license: bigscience-openrail-m
+---
+This dataset is designed to be used in testing multimodal text/image models. It's derived from cm4-10k dataset.
+
+The current splits are: `['100.unique', '100.repeat', '300.unique', '300.repeat', '1k.unique', '1k.repeat', '10k.unique', '10k.repeat']`.
+
+The `unique` ones ensure uniqueness across text entries.
+
+The `repeat` ones are repeating the same 10 unique records: - these are useful for memory leaks debugging as the records are always the same and thus remove the record variation from the equation.
+
+The default split is `100.unique`
+
+The full process of this dataset creation, including which records were used to build it, is documented inside [cm4-synthetic-testing.py](https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing/blob/main/cm4-synthetic-testing.py)

huggingface_dataset/Dataset_Card/turkish_ner.md

@@ -0,0 +1,215 @@
+---
+annotations_creators:
+- machine-generated
+language_creators:
+- expert-generated
+language:
+- tr
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: TurkishNer
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: domain
+    dtype:
+      class_label:
+        names:
+          '0': architecture
+          '1': basketball
+          '2': book
+          '3': business
+          '4': education
+          '5': fictional_universe
+          '6': film
+          '7': food
+          '8': geography
+          '9': government
+          '10': law
+          '11': location
+          '12': military
+          '13': music
+          '14': opera
+          '15': organization
+          '16': people
+          '17': religion
+          '18': royalty
+          '19': soccer
+          '20': sports
+          '21': theater
+          '22': time
+          '23': travel
+          '24': tv
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-PERSON
+          '2': I-PERSON
+          '3': B-ORGANIZATION
+          '4': I-ORGANIZATION
+          '5': B-LOCATION
+          '6': I-LOCATION
+          '7': B-MISC
+          '8': I-MISC
+  splits:
+  - name: train
+    num_bytes: 177658278
+    num_examples: 532629
+  download_size: 204393976
+  dataset_size: 177658278
+---
+
+
+# Dataset Card for turkish_ner
+
+## 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:** http://arxiv.org/abs/1702.02363
+- **Repository:** [Needs More Information]
+- **Paper:** http://arxiv.org/abs/1702.02363
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** erayyildiz@ktu.edu.tr
+
+### Dataset Summary
+
+Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+Turkish
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+There's only the training set.
+
+## 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
+
+H. Bahadir Sahin, Caglar Tirkaz, Eray Yildiz, Mustafa Tolga Eren and Omer Ozan Sonmez
+
+### Licensing Information
+
+Creative Commons Attribution 4.0 International
+
+### Citation Information
+
+@InProceedings@article{DBLP:journals/corr/SahinTYES17,
+  author    = {H. Bahadir Sahin and
+               Caglar Tirkaz and
+               Eray Yildiz and
+               Mustafa Tolga Eren and
+               Omer Ozan Sonmez},
+  title     = {Automatically Annotated Turkish Corpus for Named Entity Recognition
+               and Text Categorization using Large-Scale Gazetteers},
+  journal   = {CoRR},
+  volume    = {abs/1702.02363},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1702.02363},
+  archivePrefix = {arXiv},
+  eprint    = {1702.02363},
+  timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+### Contributions
+
+Thanks to [@merveenoyan](https://github.com/merveenoyan) for adding this dataset.