Model Card for Model ID
We fine-tuned our base model for 71 epochs on the Ca dataset, epoch 61 showed the best macro average f1 score on the evaluation dataset.
Metrics
eval_AVGf1 0.8073040334161414
eval_DIAGNOSIS.f1 0.8044417026526834
eval_DIAGNOSIS.precision 0.7774244833068362
eval_DIAGNOSIS.recall 0.8334043459735833
eval_DIAGNOSTIC.f1 0.8154647655607348
eval_DIAGNOSTIC.precision 0.7876059322033898
eval_DIAGNOSTIC.recall 0.8453666856168277
eval_DRUG.f1 0.9283865401207938
eval_DRUG.precision 0.911864406779661
eval_DRUG.recall 0.945518453427065
eval_MEDICAL_FINDING.f1 0.7855789872458644
eval_MEDICAL_FINDING.precision 0.7687839841819081
eval_MEDICAL_FINDING.recall 0.8031241931319391
eval_THERAPY.f1 0.7026481715006304
eval_THERAPY.precision 0.6716489874638379
eval_THERAPY.recall 0.7366472765732417
eval_accuracy 0.9359328085693419
eval_f1 0.7922039763638145
eval_loss 0.6178462505340576
eval_precision 0.7703492063492063
eval_recall 0.8153349909280291
eval_runtime 107.4969
eval_samples_per_second 76.114
eval_steps_per_second 9.517
test_AVGf1 0.7654950023468019
test_DIAGNOSIS.f1 0.7317784256559767
test_DIAGNOSIS.precision 0.7442550037064493
test_DIAGNOSIS.recall 0.7197132616487455
test_DIAGNOSTIC.f1 0.7815242494226328
test_DIAGNOSTIC.precision 0.7779310344827586
test_DIAGNOSTIC.recall 0.7851508120649652
test_DRUG.f1 0.9199594731509625
test_DRUG.precision 0.9013898080741231
test_DRUG.recall 0.9393103448275862
test_MEDICAL_FINDING.f1 0.7348673770120154
test_MEDICAL_FINDING.precision 0.6987497305453761
test_MEDICAL_FINDING.recall 0.7749223045661009
test_THERAPY.f1 0.6593454864924225
test_THERAPY.precision 0.6414529914529915
test_THERAPY.recall 0.6782647989154993
test_accuracy 0.9244002381348251
test_f1 0.7459972552607502
test_loss 0.7649919986724854
test_precision 0.72469725586046
test_recall 0.7685872510899022
test_runtime 124.1668
test_samples_per_second 76.421
test_steps_per_second 9.56
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Model tree for MSey/CaXLMRoBERTa-large_checkpoint-15596
Base model
FacebookAI/xlm-roberta-large