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OKAI-midi-gen-v-002

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metadata
library_name: transformers
tags:
  - generated_from_trainer
  - music
  - midi-to-midi
metrics:
  - accuracy
training_config:
  - max_position_embeddings: 1024
  - num_attention_heads: 8
  - num_hidden_layers: 8
  - sliding_window: 4
  - intermediate_size: 512
  - hidden_size: 256
  - num_key_value_heads: 4
model-index:
  - name: OKAI-midi-gen-v-002
    results: []
pipeline_tag: other

OKAI-midi-gen-v-002

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 10.7039
  • Accuracy: 0.0004

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

training_config:

  • max_position_embeddings: 1024
  • num_attention_heads: 8
  • num_hidden_layers: 8
  • sliding_window: 4
  • intermediate_size: 512
  • hidden_size: 256
  • num_key_value_heads: 4

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 444
  • gradient_accumulation_steps: 3
  • total_train_batch_size: 24
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_ratio: 0.3
  • training_steps: 2000

Training results

Training Loss Epoch Step Validation Loss Accuracy
10.2078 3.2283 100 10.2675 0.0004
9.6734 6.4565 200 10.0564 0.0030
9.1879 9.6848 300 9.9516 0.0036
8.8395 12.9130 400 9.9849 0.0038
8.4477 16.1304 500 10.0295 0.0037
7.9007 19.3587 600 10.0765 0.0023
7.2277 22.5870 700 10.1370 0.0016
6.5655 25.8152 800 10.2042 0.0011
5.8915 29.0326 900 10.2386 0.0010
5.1573 32.2609 1000 10.3026 0.0006
4.6311 35.4891 1100 10.3649 0.0006
4.1848 38.7174 1200 10.4672 0.0005
3.7458 41.9457 1300 10.5312 0.0003
3.487 45.1630 1400 10.5832 0.0003
3.206 48.3913 1500 10.6327 0.0005
3.0194 51.6196 1600 10.6676 0.0005
2.9182 54.8478 1700 10.6874 0.0005
2.9033 58.0652 1800 10.6992 0.0004
2.8987 61.2935 1900 10.7033 0.0005
2.8351 64.5217 2000 10.7039 0.0004

Framework versions

  • Transformers 4.52.3
  • Pytorch 2.6.0
  • Datasets 3.6.0
  • Tokenizers 0.21.1