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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: 0.1791
  • Precision: 0.5224
  • Recall: 0.6222
  • F1: 0.5680
  • Accuracy: 0.9631

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 0.2584 0.0 0.0 0.0 0.9365
No log 2.0 58 0.2386 0.1364 0.0133 0.0243 0.9458
No log 3.0 87 0.2312 0.2368 0.04 0.0684 0.9466
No log 4.0 116 0.1806 0.2809 0.2222 0.2481 0.9422
No log 5.0 145 0.1446 0.4453 0.2711 0.3370 0.9558
No log 6.0 174 0.1575 0.3778 0.3022 0.3358 0.9493
No log 7.0 203 0.1255 0.5081 0.4178 0.4585 0.9601
No log 8.0 232 0.1290 0.4599 0.4844 0.4719 0.9596
No log 9.0 261 0.1383 0.4844 0.4844 0.4844 0.9597
No log 10.0 290 0.1534 0.4313 0.6133 0.5064 0.9519
No log 11.0 319 0.1575 0.4423 0.6133 0.5140 0.9560
No log 12.0 348 0.1437 0.5888 0.5156 0.5498 0.9670
No log 13.0 377 0.1605 0.5 0.5911 0.5418 0.9589
No log 14.0 406 0.1529 0.5459 0.5289 0.5372 0.9640
No log 15.0 435 0.1569 0.5097 0.5867 0.5455 0.9618
No log 16.0 464 0.1656 0.4980 0.5644 0.5292 0.9607
No log 17.0 493 0.1602 0.5583 0.5956 0.5763 0.9622
0.0843 18.0 522 0.1767 0.4897 0.6356 0.5532 0.9589
0.0843 19.0 551 0.1642 0.5551 0.6044 0.5787 0.9641
0.0843 20.0 580 0.1635 0.6418 0.5733 0.6056 0.9679
0.0843 21.0 609 0.1706 0.5423 0.6267 0.5814 0.9635
0.0843 22.0 638 0.1691 0.5437 0.6089 0.5744 0.9638
0.0843 23.0 667 0.1743 0.5357 0.6 0.5660 0.9631
0.0843 24.0 696 0.1800 0.5176 0.6533 0.5776 0.9627
0.0843 25.0 725 0.1789 0.5 0.6 0.5455 0.9620
0.0843 26.0 754 0.1754 0.5388 0.5867 0.5617 0.9638
0.0843 27.0 783 0.1797 0.5164 0.6311 0.5680 0.9627
0.0843 28.0 812 0.1816 0.5321 0.6267 0.5755 0.9633
0.0843 29.0 841 0.1793 0.5222 0.6267 0.5697 0.9631
0.0843 30.0 870 0.1791 0.5224 0.6222 0.5680 0.9631

Framework versions

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