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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: 0.1618
  • Precision: 0.7352
  • Recall: 0.6436
  • F1: 0.6863
  • Accuracy: 0.9712

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: 5e-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 0.3028 0.0 0.0 0.0 0.9220
No log 2.0 58 0.2800 0.0 0.0 0.0 0.9220
No log 3.0 87 0.2136 0.2105 0.0277 0.0489 0.9302
No log 4.0 116 0.1803 0.375 0.0727 0.1217 0.9391
No log 5.0 145 0.1737 0.4923 0.2215 0.3055 0.9462
No log 6.0 174 0.1354 0.6124 0.3772 0.4668 0.9584
No log 7.0 203 0.1399 0.6062 0.4048 0.4855 0.9589
No log 8.0 232 0.1444 0.6220 0.5294 0.5720 0.9623
No log 9.0 261 0.1252 0.6439 0.6194 0.6314 0.9662
No log 10.0 290 0.1757 0.7216 0.4394 0.5462 0.9604
No log 11.0 319 0.1352 0.6707 0.5779 0.6208 0.9667
No log 12.0 348 0.1276 0.6797 0.6021 0.6385 0.9677
No log 13.0 377 0.1542 0.7328 0.5882 0.6526 0.9688
No log 14.0 406 0.1418 0.7192 0.6471 0.6812 0.9712
No log 15.0 435 0.1678 0.7162 0.5502 0.6223 0.9672
No log 16.0 464 0.1559 0.7075 0.6194 0.6605 0.9689
No log 17.0 493 0.1446 0.6568 0.6886 0.6723 0.9681
0.079 18.0 522 0.1582 0.7348 0.5848 0.6513 0.9693
0.079 19.0 551 0.1519 0.6977 0.6228 0.6581 0.9705
0.079 20.0 580 0.1503 0.7251 0.6298 0.6741 0.9703
0.079 21.0 609 0.1585 0.6834 0.6125 0.6460 0.9703
0.079 22.0 638 0.1594 0.7126 0.6263 0.6667 0.9705
0.079 23.0 667 0.1558 0.7008 0.6401 0.6691 0.9703
0.079 24.0 696 0.1570 0.7273 0.6367 0.6790 0.9708
0.079 25.0 725 0.1553 0.7022 0.6609 0.6809 0.9705
0.079 26.0 754 0.1592 0.7148 0.6332 0.6716 0.9701
0.079 27.0 783 0.1579 0.7170 0.6574 0.6859 0.9710
0.079 28.0 812 0.1597 0.7148 0.6505 0.6812 0.9708
0.079 29.0 841 0.1625 0.7309 0.6298 0.6766 0.9705
0.079 30.0 870 0.1618 0.7352 0.6436 0.6863 0.9712

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3