aa02dfb603b2dc87ed9bd26e0198a0d0

This model is a fine-tuned version of distilbert/distilbert-base-uncased-distilled-squad on the ccdv/patent-classification [abstract] dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4480
  • Data Size: 1.0
  • Epoch Runtime: 22.4982
  • Accuracy: 0.6356
  • F1 Macro: 0.5903

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 32
  • total_eval_batch_size: 32
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: constant
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Data Size Epoch Runtime Accuracy F1 Macro
No log 0 0 2.1711 0 2.0357 0.1941 0.0615
No log 1 781 1.9999 0.0078 2.3529 0.2218 0.0406
No log 2 1562 1.9046 0.0156 2.4318 0.2698 0.1046
No log 3 2343 1.6173 0.0312 2.8650 0.3702 0.2051
0.0413 4 3124 1.3973 0.0625 3.5350 0.5182 0.3476
1.3611 5 3905 1.2288 0.125 4.9177 0.5661 0.4046
1.1813 6 4686 1.1124 0.25 7.3893 0.6102 0.4789
1.01 7 5467 1.0400 0.5 12.6409 0.6426 0.5724
0.9023 8.0 6248 1.0476 1.0 23.3686 0.6585 0.5940
0.7212 9.0 7029 1.0730 1.0 22.7716 0.6581 0.6001
0.5386 10.0 7810 1.2044 1.0 22.5663 0.6412 0.5913
0.3815 11.0 8591 1.4480 1.0 22.4982 0.6356 0.5903

Framework versions

  • Transformers 4.57.0
  • Pytorch 2.8.0+cu128
  • Datasets 4.3.0
  • Tokenizers 0.22.1
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