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Yepes_0.0001_0404_ES6

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1207
  • Precision: 0.4902
  • Recall: 0.3743
  • F1: 0.4244
  • Accuracy: 0.9769

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: 0.0001
  • 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
  • training_steps: 2000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.4338 0.43 25 0.1979 0.0 0.0 0.0 0.9705
0.2051 0.86 50 0.1923 0.0 0.0 0.0 0.9705
0.1601 1.29 75 0.1618 0.0 0.0 0.0 0.9705
0.1742 1.72 100 0.1400 0.0 0.0 0.0 0.9705
0.1506 2.16 125 0.1462 0.0 0.0 0.0 0.9705
0.1507 2.59 150 0.1516 0.0 0.0 0.0 0.9705
0.1566 3.02 175 0.1382 0.0 0.0 0.0 0.9705
0.1467 3.45 200 0.1360 0.0 0.0 0.0 0.9705
0.1492 3.88 225 0.1400 0.0 0.0 0.0 0.9705
0.1543 4.31 250 0.1364 0.0 0.0 0.0 0.9705
0.1435 4.74 275 0.1384 0.0 0.0 0.0 0.9705
0.1369 5.17 300 0.1282 0.0 0.0 0.0 0.9705
0.1284 5.6 325 0.1337 0.2381 0.1198 0.1594 0.9704
0.1235 6.03 350 0.1215 0.0 0.0 0.0 0.9705
0.1165 6.47 375 0.1337 0.3613 0.1677 0.2290 0.9739
0.1184 6.9 400 0.1228 0.2303 0.1228 0.1602 0.9718
0.1076 7.33 425 0.1174 0.2646 0.3263 0.2922 0.9671
0.0964 7.76 450 0.1094 0.3972 0.2545 0.3102 0.9751
0.0902 8.19 475 0.1217 0.4264 0.2515 0.3164 0.9742
0.0891 8.62 500 0.1075 0.3746 0.3263 0.3488 0.9736
0.0813 9.05 525 0.1295 0.4354 0.2725 0.3352 0.9738
0.078 9.48 550 0.1067 0.375 0.3413 0.3574 0.9742
0.0751 9.91 575 0.1042 0.4905 0.3084 0.3787 0.9765
0.0683 10.34 600 0.1028 0.4672 0.3413 0.3945 0.9761
0.0687 10.78 625 0.1070 0.4975 0.2994 0.3738 0.9762
0.0664 11.21 650 0.1225 0.3256 0.3383 0.3319 0.9703
0.0565 11.64 675 0.1000 0.4487 0.3144 0.3697 0.9767
0.0555 12.07 700 0.1033 0.4463 0.3234 0.375 0.9757
0.045 12.5 725 0.1150 0.4237 0.3323 0.3725 0.9746
0.0514 12.93 750 0.1126 0.6 0.3503 0.4423 0.9774
0.0387 13.36 775 0.1409 0.3986 0.3473 0.3712 0.9742
0.0419 13.79 800 0.1096 0.4336 0.4401 0.4368 0.9723
0.0349 14.22 825 0.1207 0.4902 0.3743 0.4244 0.9769

Framework versions

  • Transformers 4.27.4
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.2
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