| epoch,train_precision,train_recall,train_f1,train_loss,val_precision,val_recall,val_f1,val_loss,model_name | |
| 0,0.6281179138321995,0.6438117373619988,0.6358680057388809,0.003976983747684611,0.5576036866359447,0.555045871559633,0.5563218390804598,0.006000944341604526,dmis-lab/biobert-v1.1 | |
| 1,0.7678085916258837,0.8204532248692621,0.7932584269662921,0.0023407716372105627,0.6042553191489362,0.6513761467889908,0.6269315673289182,0.005674311918063233,dmis-lab/biobert-v1.1 | |
| 2,0.8863499699338545,0.8564787914003487,0.8711583924349883,0.0012184520054125203,0.5897435897435898,0.6330275229357798,0.6106194690265486,0.006110408770417472,dmis-lab/biobert-v1.1 | |
| 3,0.8943577430972389,0.8657757117954678,0.8798346619427222,0.00102371758293386,0.56,0.5137614678899083,0.5358851674641149,0.007451768030967707,dmis-lab/biobert-v1.1 | |
| 4,0.9334126040428062,0.9122603137710633,0.922715251248898,0.0005286679887375829,0.6333333333333333,0.6100917431192661,0.6214953271028038,0.008674422153629936,dmis-lab/biobert-v1.1 | |
| 5,0.9378953421506613,0.9477048227774549,0.9427745664739884,0.0003897858330123396,0.6035242290748899,0.6284403669724771,0.6157303370786517,0.009561035372406388,dmis-lab/biobert-v1.1 | |
| 6,0.97610513739546,0.9494479953515398,0.9625920471281295,0.0002792014398714322,0.6292682926829268,0.591743119266055,0.6099290780141844,0.011409824080496596,dmis-lab/biobert-v1.1 | |
| 7,0.9802210587550901,0.979081929110982,0.9796511627906977,0.00012601296516155536,0.6442307692307693,0.6146788990825688,0.6291079812206573,0.010825570200372918,dmis-lab/biobert-v1.1 | |
| 8,0.9578587699316629,0.9773387565368972,0.9675007190106415,0.0002701500492631862,0.5477178423236515,0.6055045871559633,0.5751633986928105,0.009289918433821116,dmis-lab/biobert-v1.1 | |
| 9,0.9786743515850144,0.9866356769320163,0.9826388888888888,0.00015505489196803398,0.56,0.5779816513761468,0.5688487584650114,0.011050266518185917,dmis-lab/biobert-v1.1 | |