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README.md

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+Arabic dialects, multi-class-Classification, Tweets.
+
+# Dataset Card for Arabic_Dialect_Identification
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/Abdelrahmanrezk/dialect-prediction-with-transformers
+- **Paper:** https://arxiv.org/pdf/2005.06557.pdf
+- **Leaderboard:** [email protected]
+[email protected]
+[email protected]
+- **Point of Contact:** [email protected]
+[email protected]
+[email protected]
+
+### Dataset Summary
+
+We present QADI, an automatically collected dataset of tweets belonging to a wide range of
+country-level Arabic dialects covering 18 different countries in the Middle East and North
+Africa region. Our method for building this dataset relies on applying multiple filters to identify
+users who belong to different countries based on their account descriptions and to eliminate
+tweets that are either written in Modern Standard Arabic or contain inappropriate language. The
+resultant dataset contains 540k tweets from 2,525 users who are evenly distributed across 18 Arab countries. 
+
+### Supported Tasks and Leaderboards
+
+- Multi-class-Classification: Using extrinsic evaluation, we are able to build effective country-level dialect identification on tweets with a macro-averaged F1-score of 51.5% across 18 classes. 
+[Arabic-Dialect-Identification](https://github.com/Abdelrahmanrezk/Arabic-Dialect-Identification), rather than what used in the paper Using intrinsic evaluation, they show that the labels of a set of randomly selected tweets are 91.5% accurate. For extrinsic evaluation, they are able to build effective country-level dialect identification on tweets with a macro-averaged F1-score of 60.6% across 18 classes [ Paper](https://arxiv.org/pdf/2005.06557.pdf). And we aimed by next work to fine tune models with that data to see how the result will be.
+
+### Languages
+
+Arabic
+
+## Dataset Structure
+
+### Data Instances
+
+{'id': [1159906099585327104, 950123809608171648, 1091295506960142336],
+ 'label': [10, 14, 2],
+ 'text': [''JG 'D.J() H 'DG1*D) B/'E 'D,HF /HD \U0001f92a=2\n'D9J'D /J **9DB AJ 'DADC) J' E9DE CDH(',
+  '@FIA_WIS *0C1* E' '3EJ 9'&4) 'F' '3EJ .HD)',
+  '@showqiy @3nood_mh D' H'DDG F1H- F4,9 B71 H FA1- E9GE H4 1'JC (9/']}
+
+### Data Fields
+
+{'id': Value(dtype='int64', id=None),
+ 'label': ClassLabel(num_classes=18, names=['OM', 'SD', 'SA', 'KW', 'QA', 'LB', 'JO', 'SY', 'IQ', 'MA', 'EG', 'PL', 'YE', 'BH', 'DZ', 'AE', 'TN', 'LY'], id=None),
+ 'text': Value(dtype='string', id=None)}
+
+### Data Splits
+
+This dataset is split into a train, validation and test split. The split sizes are as follow:
+
+| Split name	|  Number of samples |
+| train | 440052 |
+| validation | 9164 |
+| test | 8981 |
+
+## 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
+
+{aabdelali,hmubarak,ysamih,sahassan2,kdarwish}@hbku.edu.qa
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+@unknown{unknown,
+author = {Abdelali, Ahmed and Mubarak, Hamdy and Samih, Younes and Hassan, Sabit and Darwish, Kareem},
+year = {2020},
+month = {05},
+pages = {},
+title = {Arabic Dialect Identification in the Wild}
+}