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ner-2

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-2
    results: []

ner-2

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1804
  • Precision: 0.6443
  • Recall: 0.5708
  • F1: 0.6053
  • Accuracy: 0.9691

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: 7e-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 29 0.2727 0.0 0.0 0.0 0.9392
No log 2.0 58 0.2246 0.1163 0.0228 0.0382 0.9383
No log 3.0 87 0.1744 0.3718 0.1324 0.1953 0.9480
No log 4.0 116 0.1492 0.4734 0.3653 0.4124 0.9569
No log 5.0 145 0.1472 0.4905 0.4703 0.4802 0.9581
No log 6.0 174 0.1320 0.5403 0.5205 0.5302 0.9618
No log 7.0 203 0.1423 0.5922 0.5571 0.5741 0.9667
No log 8.0 232 0.1616 0.5838 0.5251 0.5529 0.9648
No log 9.0 261 0.1443 0.6082 0.5388 0.5714 0.9676
No log 10.0 290 0.1681 0.5990 0.5662 0.5822 0.9654
No log 11.0 319 0.1611 0.4853 0.6027 0.5377 0.9599
No log 12.0 348 0.1751 0.4887 0.5936 0.5361 0.9588
No log 13.0 377 0.1796 0.4819 0.6073 0.5374 0.9593
No log 14.0 406 0.1609 0.6760 0.5525 0.6080 0.9699
No log 15.0 435 0.1821 0.5136 0.6027 0.5546 0.9606
No log 16.0 464 0.1581 0.6462 0.5753 0.6087 0.9691
No log 17.0 493 0.1582 0.6531 0.5845 0.6169 0.9692
0.0763 18.0 522 0.1641 0.5574 0.6210 0.5875 0.9648
0.0763 19.0 551 0.1681 0.5671 0.5982 0.5822 0.9663
0.0763 20.0 580 0.1710 0.5917 0.5890 0.5904 0.9667
0.0763 21.0 609 0.1794 0.6703 0.5662 0.6139 0.9702
0.0763 22.0 638 0.1759 0.6103 0.5936 0.6019 0.9672
0.0763 23.0 667 0.1762 0.6298 0.5982 0.6136 0.9687
0.0763 24.0 696 0.1811 0.6176 0.5753 0.5957 0.9681
0.0763 25.0 725 0.1793 0.6337 0.5845 0.6081 0.9696
0.0763 26.0 754 0.1794 0.6796 0.5616 0.615 0.9702
0.0763 27.0 783 0.1776 0.6293 0.5890 0.6085 0.9692
0.0763 28.0 812 0.1796 0.6443 0.5708 0.6053 0.9694
0.0763 29.0 841 0.1803 0.6410 0.5708 0.6039 0.9692
0.0763 30.0 870 0.1804 0.6443 0.5708 0.6053 0.9691

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3