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xlnet-base-cased_fold_6_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: 1.6214
  • F1: 0.8352

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 1.0 290 0.4174 0.7980
0.4661 2.0 580 0.4118 0.8142
0.4661 3.0 870 0.5152 0.8331
0.2714 4.0 1160 0.6901 0.8242
0.2714 5.0 1450 0.6853 0.8451
0.1542 6.0 1740 0.8570 0.8399
0.0935 7.0 2030 1.1342 0.8401
0.0935 8.0 2320 1.1763 0.8397
0.037 9.0 2610 1.3530 0.8215
0.037 10.0 2900 1.3826 0.8402
0.0351 11.0 3190 1.4057 0.8374
0.0351 12.0 3480 1.4259 0.8455
0.0159 13.0 3770 1.4270 0.8431
0.0249 14.0 4060 1.4215 0.8442
0.0249 15.0 4350 1.4245 0.8408
0.0197 16.0 4640 1.4171 0.8353
0.0197 17.0 4930 1.4537 0.8383
0.0137 18.0 5220 1.4786 0.8430
0.0068 19.0 5510 1.5635 0.8443
0.0068 20.0 5800 1.5527 0.8378
0.0062 21.0 6090 1.5917 0.8460
0.0062 22.0 6380 1.6317 0.8318
0.005 23.0 6670 1.6226 0.8340
0.005 24.0 6960 1.6378 0.8310
0.007 25.0 7250 1.6214 0.8352

Framework versions

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