--- 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