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GerMedBert_NORMTOP50_V02_BRONCO

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metadata
license: apache-2.0
base_model: GerMedBERT/medbert-512
tags:
  - medical
metrics:
  - precision
  - recall
  - accuracy
model-index:
  - name: GerMedBert_NORMTOP50_V02_BRONCO
    results: []
datasets:
  - bigbio/bronco
language:
  - de

GerMedBert_NORMTOP50_V02_BRONCO

This model is a fine-tuned version of GerMedBERT/medbert-512 on the BRONCO150 dataset. The task is to normalize entities into the top50 codes from the ICD10GM, OPS and ATC catalogue. It achieves the following results on the evaluation set:

  • Loss: 0.0166
  • F1 Score: 0.8624
  • Precision: 0.8939
  • Recall: 0.8329
  • Accuracy: 0.8594
  • Num Input Tokens Seen: 15165836

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: 20

Training results

Training Loss Epoch Step Validation Loss F1 Score Precision Recall Accuracy Input Tokens Seen
No log 0.25 81 0.1649 0.0 1.0 0.0 0.5920 190512
0.2195 0.5 162 0.0988 0.0 1.0 0.0 0.5920 380384
0.2195 0.75 243 0.0825 0.0 1.0 0.0 0.5920 570256
0.0855 1.0 324 0.0768 0.0 1.0 0.0 0.5920 759687
0.0855 1.25 405 0.0744 0.0 1.0 0.0 0.5920 948599
0.0737 1.5 486 0.0688 0.0 1.0 0.0 0.5920 1138791
0.0737 1.75 567 0.0652 0.1979 0.94 0.1106 0.6059 1327703
0.0666 2.0 648 0.0600 0.0982 0.9565 0.0518 0.5972 1516814
0.0666 2.25 729 0.0553 0.1231 0.9333 0.0659 0.5972 1706686
0.0572 2.5 810 0.0521 0.4093 0.8394 0.2706 0.6458 1896878
0.0572 2.75 891 0.0490 0.4326 0.8777 0.2871 0.6510 2086110
0.0499 3.0 972 0.0456 0.2897 1.0 0.1694 0.6476 2275221
0.0499 3.25 1053 0.0423 0.5060 0.925 0.3482 0.6667 2465413
0.0441 3.5 1134 0.0406 0.5386 0.8913 0.3859 0.6736 2654965
0.0441 3.75 1215 0.0396 0.5979 0.8419 0.4635 0.6858 2845157
0.0393 4.0 1296 0.0377 0.6517 0.9004 0.5106 0.7083 3034556
0.0393 4.25 1377 0.0357 0.6319 0.9075 0.4847 0.6997 3224428
0.0361 4.5 1458 0.0346 0.6154 0.9245 0.4612 0.7066 3414620
0.0361 4.75 1539 0.0334 0.6258 0.8987 0.48 0.7101 3604172
0.032 5.0 1620 0.0321 0.6775 0.9124 0.5388 0.7292 3793603
0.032 5.25 1701 0.0306 0.7081 0.9176 0.5765 0.7378 3983155
0.0293 5.5 1782 0.0302 0.6928 0.9019 0.5624 0.7361 4172387
0.0293 5.75 1863 0.0292 0.6657 0.9247 0.52 0.7240 4362579
0.0273 6.0 1944 0.0287 0.7365 0.9253 0.6118 0.7691 4552330
0.0273 6.25 2025 0.0275 0.7215 0.9328 0.5882 0.7552 4741882
0.0258 6.5 2106 0.0272 0.7275 0.9024 0.6094 0.7517 4930794
0.0258 6.75 2187 0.0260 0.7451 0.9204 0.6259 0.7726 5120026
0.0228 7.0 2268 0.0260 0.7247 0.9203 0.5976 0.7656 5309137
0.0228 7.25 2349 0.0249 0.7867 0.9077 0.6941 0.7969 5499649
0.0218 7.5 2430 0.0246 0.7572 0.9079 0.6494 0.7778 5688881
0.0218 7.75 2511 0.0239 0.7779 0.9088 0.68 0.7882 5878113
0.02 8.0 2592 0.0239 0.7835 0.8994 0.6941 0.7899 6067224
0.02 8.25 2673 0.0229 0.7711 0.9159 0.6659 0.7917 6256456
0.0184 8.5 2754 0.0227 0.7705 0.8969 0.6753 0.7917 6446328
0.0184 8.75 2835 0.0226 0.7782 0.8671 0.7059 0.7899 6636520
0.0182 9.0 2916 0.0224 0.7937 0.8988 0.7106 0.8003 6825951
0.0182 9.25 2997 0.0217 0.7815 0.8939 0.6941 0.7951 7015183
0.0172 9.5 3078 0.0213 0.8156 0.9101 0.7388 0.8212 7205375
0.0172 9.75 3159 0.0211 0.8063 0.9086 0.7247 0.8142 7394927
0.0154 10.0 3240 0.0216 0.8246 0.8820 0.7741 0.8212 7583366
0.0154 10.25 3321 0.0204 0.7831 0.8943 0.6965 0.8021 7772598
0.0145 10.5 3402 0.0201 0.8185 0.9034 0.7482 0.8229 7962470
0.0145 10.75 3483 0.0200 0.8261 0.9048 0.76 0.8264 8152662
0.0143 11.0 3564 0.0198 0.8238 0.8929 0.7647 0.8281 8341773
0.0143 11.25 3645 0.0196 0.8229 0.8972 0.76 0.8264 8531645
0.0131 11.5 3726 0.0193 0.8231 0.8817 0.7718 0.8212 8720877
0.0131 11.75 3807 0.0195 0.8152 0.8822 0.7576 0.8177 8910109
0.0129 12.0 3888 0.0192 0.8263 0.9119 0.7553 0.8299 9099860
0.0129 12.25 3969 0.0188 0.8229 0.8972 0.76 0.8212 9289412
0.0116 12.5 4050 0.0191 0.8123 0.8883 0.7482 0.8247 9479284
0.0116 12.75 4131 0.0181 0.8417 0.9030 0.7882 0.8472 9669156
0.0115 13.0 4212 0.0180 0.8398 0.8895 0.7953 0.8420 9857947
0.0115 13.25 4293 0.0177 0.8445 0.9126 0.7859 0.8455 10045899
0.0108 13.5 4374 0.0179 0.8426 0.8901 0.8 0.8438 10236091
0.0108 13.75 4455 0.0179 0.8519 0.8961 0.8118 0.8524 10426283
0.0103 14.0 4536 0.0177 0.8392 0.9003 0.7859 0.8420 10615394
0.0103 14.25 4617 0.0176 0.8603 0.9062 0.8188 0.8594 10805906
0.0097 14.5 4698 0.0173 0.8475 0.904 0.7976 0.8507 10995458
0.0097 14.75 4779 0.0175 0.8511 0.9003 0.8071 0.8524 11185650
0.0095 15.0 4860 0.0173 0.8501 0.8979 0.8071 0.8490 11375081
0.0095 15.25 4941 0.0175 0.8451 0.8927 0.8024 0.8472 11564633
0.009 15.5 5022 0.0174 0.8483 0.8912 0.8094 0.8490 11754185
0.009 15.75 5103 0.0172 0.8490 0.8956 0.8071 0.8438 11943417
0.009 16.0 5184 0.0174 0.8547 0.8883 0.8235 0.8490 12132528
0.009 16.25 5265 0.0171 0.8519 0.8961 0.8118 0.8490 12322400
0.0085 16.5 5346 0.0169 0.8537 0.8861 0.8235 0.8524 12511632
0.0085 16.75 5427 0.0169 0.8498 0.8915 0.8118 0.8524 12701184
0.0084 17.0 5508 0.0169 0.8487 0.8892 0.8118 0.8507 12889655
0.0084 17.25 5589 0.0167 0.8634 0.8962 0.8329 0.8524 13079527
0.0079 17.5 5670 0.0168 0.8504 0.8958 0.8094 0.8472 13269399
0.0079 17.75 5751 0.0168 0.8606 0.8957 0.8282 0.8594 13458631
0.0081 18.0 5832 0.0167 0.8564 0.8949 0.8212 0.8542 13648382
0.0081 18.25 5913 0.0166 0.8620 0.8959 0.8306 0.8576 13838254
0.0078 18.5 5994 0.0166 0.8533 0.8964 0.8141 0.8542 14028126
0.0078 18.75 6075 0.0167 0.8610 0.8937 0.8306 0.8611 14217678
0.0078 19.0 6156 0.0166 0.8610 0.8937 0.8306 0.8594 14407109
0.0078 19.25 6237 0.0166 0.8637 0.8942 0.8353 0.8594 14596341
0.0072 19.5 6318 0.0166 0.8620 0.8959 0.8306 0.8594 14786213
0.0072 19.75 6399 0.0166 0.8624 0.8939 0.8329 0.8594 14976085
0.008 20.0 6480 0.0166 0.8624 0.8939 0.8329 0.8594 15165836

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1