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xlnet-base-cased_fold_4_binary_v1

This model is a fine-tuned version of xlnet-base-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.5724
  • F1: 0.8315

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: 2e-05
  • 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: 25

Training results

Training Loss Epoch Step Validation Loss F1
No log 1.0 289 0.4043 0.8009
0.4373 2.0 578 0.4093 0.8260
0.4373 3.0 867 0.5084 0.8206
0.2707 4.0 1156 0.5945 0.8087
0.2707 5.0 1445 0.6389 0.8251
0.1691 6.0 1734 0.8131 0.8156
0.1012 7.0 2023 0.9865 0.8190
0.1012 8.0 2312 1.1356 0.8342
0.0506 9.0 2601 1.0624 0.8369
0.0506 10.0 2890 1.2604 0.8255
0.0384 11.0 3179 1.2648 0.8183
0.0384 12.0 3468 1.3763 0.8158
0.0318 13.0 3757 1.4966 0.8217
0.0221 14.0 4046 1.3889 0.8250
0.0221 15.0 4335 1.4014 0.8284
0.0145 16.0 4624 1.5321 0.8289
0.0145 17.0 4913 1.4914 0.8233
0.0172 18.0 5202 1.3946 0.8314
0.0172 19.0 5491 1.5032 0.8269
0.0135 20.0 5780 1.5111 0.8328
0.0087 21.0 6069 1.4899 0.8318
0.0087 22.0 6358 1.5562 0.8311
0.0061 23.0 6647 1.5384 0.8327
0.0061 24.0 6936 1.5798 0.8304
0.0052 25.0 7225 1.5724 0.8315

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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