Update README.md

README.md

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 ## Dataset Summary
 
-MelodyHub is a curated dataset essential for training MelodyT5, containing 261,900 melodies formatted in ABC notation and sourced from public sheet music datasets and online platforms. It includes folk songs and other non-copyrighted musical scores, ensuring diversity across traditions and epochs. The dataset includes seven melody-centric tasks: cataloging, generation, harmonization, melodization, segmentation, transcription, and variation. These tasks result in over one million task instances, providing a comprehensive resource for symbolic music processing. Each task is presented in a score-to-score format with task identifiers included in the input data. MelodyHub's rigorous curation process ensures high-quality, consistent data suitable for developing and evaluating symbolic music models. 
+MelodyHub is a curated dataset essential for training [MelodyT5](https://huggingface.co/sander-wood/melodyt5), containing 261,900 melodies formatted in ABC notation and sourced from public sheet music datasets and online platforms. It includes folk songs and other non-copyrighted musical scores, ensuring diversity across traditions and epochs. The dataset includes seven melody-centric tasks: cataloging, generation, harmonization, melodization, segmentation, transcription, and variation. These tasks result in over one million task instances, providing a comprehensive resource for symbolic music processing. Each task is presented in a score-to-score format with task identifiers included in the input data. MelodyHub's rigorous curation process ensures high-quality, consistent data suitable for developing and evaluating symbolic music models. 
 
 ## ABC Notation
 
@@ -74,7 +74,7 @@ Following the curation of melody data, the MelodyHub dataset was segmented into
 - **Variation:**
   This task centres on data from The Session, wherein each ABC notation file may contain multiple variants of the same tune. Tunes with two or more variations are selected, with every possible pair of variants utilized as both input and output. The output initiates with an `E:` field signifying the extent of disparities between the input and output scores, with lower values suggesting substantial variations in the musical scores.
 
-Together, these tasks encompass 1,067,747 instances, spanning analytical to generative challenges in Music Information Retrieval (MIR). This comprehensive dataset serves as a valuable resource for developing and evaluating symbolic music models like [MelodyT5](https://huggingface.co/sander-wood/melodyt5).
+Together, these tasks encompass 1,067,747 instances, spanning analytical to generative challenges in Music Information Retrieval (MIR). This comprehensive dataset serves as a valuable resource for developing and evaluating symbolic music models like MelodyT5.
 
 ## Copyright Disclaimer
 This dataset is for research use only and not for commercial purposes. We believe all data in this dataset is in the public domain. If you own the copyright to any musical composition in the MelodyHub dataset and have concerns, please contact us at [email protected]. We will address your concerns and take appropriate action if needed.