readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: music-generation
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # music-generation
  
  This model a trained from scratch version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset where the text represents musical notes. The [dataset](https://www.kaggle.com/datasets/soumikrakshit/classical-music-midi) consists of one stream of notes from MIDI files (the stream with most notes), where all of the melodies were transposed either to C major or A minor. Also, the BPM of the song is ignored, the duration of each note is based on its quarter length.
  
  Each element in the melody is represented by a series of letters and numbers with the following structure.
  * For a note: ns[pitch of the note as a string]s[duration]
    * Examples: nsC4s0p25, nsF7s1p0, 
  * For a rest: rs[duration]:
    * Examples: rs0p5, rs1q6
  * For a chord: cs[number of notes in chord]s[pitches of chords separated by "s"]s[duration]
    * Examples: cs2sE7sF7s1q3, cs2sG3sGw3s0p25
    
  The following special symbols are replaced in the strings by the following:
  * . = p
  * / = q
  * # = 
  * - = t
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0005
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  