Create README.md

README.md

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+TODO: Add YAML tags here. 
+---
+name: **TRBLLmaker** 
+
+annotations_creators: found
+
+language_creators: found
+
+languages: en-US
+
+licenses: Genius-Ventura-Toker
+
+multilinguality: monolingual
+
+source_datasets: original
+
+task_categories: sequence-modeling
+
+task_ids: sequence-modeling-seq2seq_generate
+
+
+# Dataset Card for TRBLLmaker Dataset
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [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)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** https://github.com/venturamor/TRBLLmaker-NLP
+- **Paper:** in git
+
+### Dataset Summary
+TRBLLmaker - To Read Between Lyrics Lines.
+Dataset used in order to train a model to get as an input - several lines of song's lyrics and generate optional interpretation / meaning of them or use the songs' metdata for various tasks such as classification.
+
+This dataset is based on 'Genius' website's data, which contains global collection of songs lyrics and provides annotations and interpretations to songs lyrics and additional music knowledge.
+We used 'Genius' API, created private client and extracted the relevant raw data from Genius servers.
+
+We extracted the songs by the most popular songs in each genre - pop, rap, rock, country and r&b. Afterwards, we created a varied pool of 150 artists that associated with different music styles and periods, and extracted maximum of 100 samples from each.
+We combined all the data, without repetitions, into one final database. After preforming a cleaning of non-English lyrics, we got our final corpus that contains 8,808 different songs with over all of 60,630 samples, while each sample is a specific sentence from the song's lyrics and its top rated annotation.
+
+### Supported Tasks and Leaderboards
+
+Seq2Seq
+
+### Languages
+
+[En] - English
+
+## Dataset Structure
+
+### Data Fields
+
+We stored each sample in a 'SongInfo' structure with the following attributes: title, genre, annotations and song's meta data. 
+The meta data contains the artist's name, song id in the server, lyrics and statistics such page views.
+
+### Data Splits
+
+train [0.64 (0.8 * 0.8)], test[0.2], validation [0.16 (0.8 * 0.2)]
+
+## Dataset Creation
+
+### Source Data
+Genius - https://genius.com/
+
+### Annotations
+
+#### Who are the annotators?
+
+top-ranked annotations by users in Genoius websites / Official Genius annotations
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+We are excited about the future of applying attention-based models on task such as meaning generation. 
+We hope this dataset will encourage more NLP researchers to improve the way we understand and enjoy songs, since
+achieving artistic comprehension is another step that progress us to the goal of robust AI. 
+
+### Other Known Limitations
+
+The artists list can be found here.
+
+## Additional Information
+
+### Dataset Curators
+
+This Dataset created by Mor Ventura and Michael Toker.
+
+### Licensing Information
+
+All source of data belongs to Genius.
+
+### Contributions
+
+Thanks to [@venturamor, @tokeron](https://github.com/venturamor/TRBLLmaker-NLP) for adding this dataset.