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cdp-multi-classifier-sub-classes-weighted

This model is a fine-tuned version of alex-miller/ODABert. It achieves the following results on the evaluation set:

  • Loss: 0.6494
  • Accuracy: 0.8423
  • F1: 0.7847
  • Precision: 0.7488
  • Recall: 0.8243

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-06
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.8824 1.0 206 0.9109 0.6793 0.5494 0.5385 0.5607
0.816 2.0 412 0.8348 0.7865 0.7127 0.6712 0.7597
0.7455 3.0 618 0.7694 0.7712 0.7053 0.64 0.7855
0.6808 4.0 824 0.7323 0.7874 0.7217 0.6638 0.7907
0.6246 5.0 1030 0.6920 0.7829 0.7175 0.6567 0.7907
0.5689 6.0 1236 0.6926 0.7874 0.7230 0.6624 0.7959
0.5237 7.0 1442 0.6642 0.8072 0.7476 0.6876 0.8191
0.4842 8.0 1648 0.6419 0.8081 0.7461 0.6925 0.8088
0.4519 9.0 1854 0.6498 0.8225 0.7612 0.7169 0.8114
0.4264 10.0 2060 0.6496 0.8315 0.7728 0.7294 0.8217
0.4078 11.0 2266 0.6669 0.8378 0.7800 0.7401 0.8243
0.398 12.0 2472 0.6508 0.8405 0.7828 0.7453 0.8243
0.3808 13.0 2678 0.6539 0.8387 0.7809 0.7419 0.8243
0.3757 14.0 2884 0.6497 0.8423 0.7847 0.7488 0.8243
0.3691 15.0 3090 0.6494 0.8423 0.7847 0.7488 0.8243

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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