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TODO: Add YAML tags here. |
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name: **TRBLLmaker** |
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annotations_creators: found |
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language_creators: found |
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languages: en-US |
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licenses: Genius-Ventura-Toker |
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multilinguality: monolingual |
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source_datasets: original |
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task_categories: sequence-modeling |
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task_ids: sequence-modeling-seq2seq_generate |
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# Dataset Card for TRBLLmaker Dataset |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Split info](#Split-info) |
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- [Dataset Creation](#dataset-creation) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Repository:** https://github.com/venturamor/TRBLLmaker-NLP |
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- **Paper:** in git |
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### Dataset Summary |
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TRBLLmaker - To Read Between Lyrics Lines. |
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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. |
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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. |
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We used 'Genius' API, created private client and extracted the relevant raw data from Genius servers. |
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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. |
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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. |
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### Supported Tasks and Leaderboards |
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Seq2Seq |
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### Languages |
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[En] - English |
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## Dataset Structure |
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### Data Fields |
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We stored each sample in a 'SongInfo' structure with the following attributes: title, genre, annotations and song's meta data. |
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The meta data contains the artist's name, song id in the server, lyrics and statistics such page views. |
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### Data Splits |
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train |
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train_songs |
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test |
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test_songs |
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validation |
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validation songs |
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## Split info |
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- songs |
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- samples |
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train [0.64 (0.8 * 0.8)], test[0.2], validation [0.16 (0.8 * 0.2)] |
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## Dataset Creation |
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### Source Data |
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Genius - https://genius.com/ |
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### Annotations |
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#### Who are the annotators? |
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top-ranked annotations by users in Genoius websites / Official Genius annotations |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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We are excited about the future of applying attention-based models on task such as meaning generation. |
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We hope this dataset will encourage more NLP researchers to improve the way we understand and enjoy songs, since |
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achieving artistic comprehension is another step that progress us to the goal of robust AI. |
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### Other Known Limitations |
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The artists list can be found here. |
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## Additional Information |
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### Dataset Curators |
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This Dataset created by Mor Ventura and Michael Toker. |
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### Licensing Information |
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All source of data belongs to Genius. |
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### Contributions |
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Thanks to [@venturamor, @tokeron](https://github.com/venturamor/TRBLLmaker-NLP) for adding this dataset. |
