Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
-license: apache-2.0
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license:
+- apache-2.0
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+pretty_name: MTG Jamendo
 ---
+# Dataset Card for MTG Jamendo Dataset
+
+## Dataset Description
+- **Repository:** [MTG Jamendo dataset repository](https://github.com/MTG/mtg-jamendo-dataset)
+
+### Dataset Summary
+
+MTG-Jamendo Dataset, a new open dataset for music auto-tagging. It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. The dataset contains over 55,000 full audio tracks with 195 tags from genre, instrument, and mood/theme categories. We provide elaborated data splits for researchers and report the performance of a simple baseline approach on five different sets of tags: genre, instrument, mood/theme, top-50, and overall.
+
+## Dataset structure
+
+### Data Fields
+
+- `id`: an integer containing the id of the track
+- `artist_id`: an integer containing the id of the artist
+- `album_id`: an integer containing the id of the album
+- `duration_in_sec`: duration of the track as a float
+- `genres`: list of strings, describing genres the track is assigned to
+- `instruments`: list of strings for the main instruments of the track
+- `moods`: list of strings, describing the moods the track is assigned to
+- `audio`: audio of the track
+
+### Data Splits
+
+This dataset has 2 balanced splits: _train_ (90%) and _validation_ (10%)
+
+
+### Licensing Information
+
+This dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). 
+
+### Citation Information
+
+```
+@conference {bogdanov2019mtg,
+    author = "Bogdanov, Dmitry and Won, Minz and Tovstogan, Philip and Porter, Alastair and Serra, Xavier",
+    title = "The MTG-Jamendo Dataset for Automatic Music Tagging",
+    booktitle = "Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019)",
+    year = "2019",
+    address = "Long Beach, CA, United States",
+    url = "http://hdl.handle.net/10230/42015"
+}
+```