library_name: transformers
language:
- de
base_model:
- GerMedBERT/medbert-512
pipeline_tag: token-classification
Model Card for Model ID
We fine-tuned our base model for 71 epochs on the Ca dataset, epoch 68 showed the best macro average f1 score on the evaluation dataset.
Metrics
eval_AVGf1 0.8032336746529752
eval_DIAGNOSIS.f1 0.7955801104972375
eval_DIAGNOSIS.precision 0.7656557699881843
eval_DIAGNOSIS.recall 0.82793867120954
eval_DIAGNOSTIC.f1 0.8097188097188096
eval_DIAGNOSTIC.precision 0.7797055730809674
eval_DIAGNOSTIC.recall 0.8421351504826803
eval_DRUG.f1 0.9214929214929215
eval_DRUG.precision 0.9002514668901928
eval_DRUG.recall 0.9437609841827768
eval_MEDICAL_FINDING.f1 0.7812833218340337
eval_MEDICAL_FINDING.precision 0.7604395604395604
eval_MEDICAL_FINDING.recall 0.8033019476331743
eval_THERAPY.f1 0.7080932097218742
eval_THERAPY.precision 0.6731777036684136
eval_THERAPY.recall 0.7468287526427061
eval_accuracy 0.9415681083480303
eval_f1 0.788057764075937
eval_loss 0.46635299921035767
eval_precision 0.7625447465929787
eval_recall 0.8153370937416062
eval_runtime 36.5944
eval_samples_per_second 223.586
eval_steps_per_second 27.955
test_AVGf1 0.765773820622575
test_DIAGNOSIS.f1 0.7267739575713241
test_DIAGNOSIS.precision 0.742803738317757
test_DIAGNOSIS.recall 0.711421410669531
test_DIAGNOSTIC.f1 0.7813144034806503
test_DIAGNOSTIC.precision 0.77124773960217
test_DIAGNOSTIC.recall 0.7916473317865429
test_DRUG.f1 0.9209993247805537
test_DRUG.precision 0.9021164021164021
test_DRUG.recall 0.9406896551724138
test_MEDICAL_FINDING.f1 0.7354366197183099
test_MEDICAL_FINDING.precision 0.6959164089988271
test_MEDICAL_FINDING.recall 0.7797156851033329
test_THERAPY.f1 0.6643447975620373
test_THERAPY.precision 0.6411764705882353
test_THERAPY.recall 0.6892502258355917
test_accuracy 0.9330358352068041
test_f1 0.7461369909791981
test_loss 0.5957663655281067
test_precision 0.7219958145170173
test_recall 0.7719484190072425
test_runtime 42.5823
test_samples_per_second 222.839
test_steps_per_second 27.875