Model Training Metrics

Step	Training Loss	Validation Loss	Precision	Recall	F1	Accuracy
4500	0.051600	0.090031	0.595145	0.597476	0.596308	0.984089
4600	0.051600	0.088728	0.600799	0.588773	0.594725	0.984254
4700	0.051600	0.083724	0.624070	0.620540	0.622300	0.984184
4800	0.051600	0.089457	0.634303	0.645344	0.639776	0.984478
4900	0.051600	0.087367	0.626459	0.630548	0.628497	0.984054
5000	0.049400	0.084299	0.617598	0.644473	0.630750	0.983748
5100	0.049400	0.086022	0.631395	0.628372	0.629880	0.984454
5200	0.049400	0.086624	0.631912	0.651436	0.641526	0.984184
5300	0.049400	0.087849	0.641918	0.658399	0.650054	0.984607
5400	0.049400	0.090842	0.648156	0.634900	0.641460	0.984254
5500	0.047100	0.089992	0.627105	0.664491	0.645257	0.984019
5600	0.047100	0.085330	0.638217	0.654047	0.646035	0.984560
5700	0.047100	0.084110	0.644924	0.649695	0.647301	0.983984
5800	0.047100	0.085505	0.642038	0.657963	0.649903	0.984584
5900	0.047100	0.086117	0.629599	0.670148	0.649241	0.984572
6000	0.037200	0.087370	0.640464	0.648825	0.644617	0.984337
6100	0.037200	0.084265	0.624186	0.667102	0.644931	0.984442
6200	0.037200	0.084804	0.636364	0.676240	0.655696	0.984678
6300	0.037200	0.083215	0.638559	0.655788	0.647059	0.984560
6400	0.037200	0.083421	0.658244	0.668842	0.663501	0.985078
6500	0.037600	0.085991	0.637644	0.650131	0.643827	0.984478
6600	0.037600	0.086048	0.675829	0.674064	0.674946	0.985184
6700	0.037600	0.085995	0.660017	0.662315	0.661164	0.985113
6800	0.037600	0.085964	0.663314	0.660139	0.661723	0.985549
6900	0.037600	0.084587	0.655518	0.682332	0.668657	0.985643
7000	0.035200	0.084067	0.656785	0.684508	0.670360	0.985325
7100	0.035200	0.083450	0.658670	0.681027	0.669662	0.985631
7200	0.035200	0.087981	0.665669	0.677546	0.671555	0.985243
7300	0.035200	0.081362	0.659746	0.677546	0.668527	0.985972
7400	0.035200	0.083816	0.646620	0.678416	0.662136	0.985678
7500	0.031600	0.084738	0.658670	0.681027	0.669662	0.985572
7600	0.031600	0.084099	0.669345	0.688860	0.678962	0.985725
7700	0.031600	0.085109	0.673250	0.682332	0.677761	0.985855
7800	0.031600	0.085043	0.650648	0.677546	0.663824	0.985219
7900	0.031600	0.086399	0.671350	0.686249	0.678717	0.985513
8000	0.032300	0.084272	0.668826	0.674064	0.671435	0.985961
8100	0.032300	0.086206	0.656171	0.680157	0.667949	0.985337
8200	0.032300	0.085437	0.667521	0.679721	0.673566	0.985972
8300	0.032300	0.084903	0.679565	0.680157	0.679861	0.986090
8400	0.032300	0.084208	0.661384	0.690165	0.675468	0.985890
8500	0.030700	0.084594	0.674033	0.690165	0.682004	0.986149
8600	0.030700	0.085151	0.678280	0.686249	0.682241	0.986337
8700	0.030700	0.085606	0.669635	0.687119	0.678265	0.986008
8800	0.030700	0.085640	0.669499	0.685814	0.677558	0.985902
8900	0.030700	0.086006	0.671496	0.685814	0.678579	0.985855
9000	0.028000	0.085893	0.672208	0.686249	0.679156	0.985925