Model description

More information needed

Intended uses & limitations

More information needed

More information needed

The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9779	0.08	100	1.1158	0.6041
0.9934	0.16	200	1.0227	0.6501
0.9562	0.24	300	0.9276	0.6748
1.0995	0.32	400	0.9088	0.6836
0.8198	0.39	500	0.8581	0.6949
0.8034	0.47	600	0.8444	0.6967
0.8319	0.55	700	0.8196	0.7148
0.787	0.63	800	0.8360	0.6975
0.8642	0.71	900	0.8250	0.7008
0.8329	0.79	1000	0.7939	0.7172
0.9678	0.87	1100	0.7661	0.7332
0.8226	0.95	1200	0.7284	0.7373
0.7967	1.03	1300	0.7355	0.7411
0.6531	1.1	1400	0.7561	0.7247
0.5719	1.18	1500	0.6839	0.7638
0.6123	1.26	1600	0.6857	0.7584
0.6504	1.34	1700	0.6970	0.7531
0.6214	1.42	1800	0.6841	0.7576
0.4925	1.5	1900	0.6624	0.7642
0.5797	1.58	2000	0.6287	0.7709
0.6018	1.66	2100	0.6537	0.7622
0.6334	1.74	2200	0.6413	0.7713
0.4111	1.82	2300	0.6242	0.7786
0.4779	1.89	2400	0.6260	0.7790
0.5488	1.97	2500	0.6146	0.7807
0.3212	2.05	2600	0.6975	0.7707
0.4282	2.13	2700	0.6344	0.7790
0.2822	2.21	2800	0.6985	0.7845
0.3003	2.29	2900	0.5954	0.7993
0.2982	2.37	3000	0.6156	0.7940
0.2628	2.45	3100	0.6318	0.7963
0.2987	2.53	3200	0.6495	0.8030
0.2714	2.6	3300	0.6018	0.8052
0.3059	2.68	3400	0.5944	0.8078
0.2762	2.76	3500	0.6296	0.7936
0.3685	2.84	3600	0.6277	0.8017
0.2299	2.92	3700	0.5834	0.8125
0.3414	3.0	3800	0.5750	0.8167
0.1082	3.08	3900	0.6201	0.8196
0.049	3.16	4000	0.6475	0.8161
0.102	3.24	4100	0.6791	0.8097
0.0483	3.31	4200	0.6582	0.8216
0.1204	3.39	4300	0.6603	0.8222
0.0611	3.47	4400	0.7174	0.8190
0.0555	3.55	4500	0.6841	0.8236
0.0188	3.63	4600	0.7009	0.8240
0.1292	3.71	4700	0.7040	0.8204
0.0661	3.79	4800	0.7074	0.8238
0.1061	3.87	4900	0.6984	0.8210
0.0861	3.95	5000	0.6913	0.8230