Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0519	0.09	300	0.0367	0.9736	0.9691	0.9713	0.9856
0.0518	0.17	600	0.0379	0.9717	0.9709	0.9713	0.9855
0.048	0.26	900	0.0357	0.9742	0.9692	0.9717	0.9858
0.0478	0.34	1200	0.0350	0.9736	0.9724	0.9730	0.9863
0.0495	0.43	1500	0.0366	0.9734	0.9703	0.9718	0.9856
0.0457	0.51	1800	0.0344	0.9719	0.9749	0.9734	0.9863
0.0464	0.6	2100	0.0347	0.9731	0.9717	0.9724	0.9861
0.0447	0.68	2400	0.0329	0.9743	0.9739	0.9741	0.9868
0.0435	0.77	2700	0.0332	0.9738	0.9748	0.9743	0.9868
0.0414	0.85	3000	0.0324	0.9729	0.9771	0.9750	0.9871
0.0412	0.94	3300	0.0312	0.9759	0.9756	0.9758	0.9875
0.0352	1.02	3600	0.0312	0.9749	0.9760	0.9754	0.9875
0.0353	1.11	3900	0.0304	0.9767	0.9759	0.9763	0.9878
0.0348	1.19	4200	0.0305	0.9765	0.9748	0.9757	0.9877
0.0362	1.28	4500	0.0313	0.9768	0.9738	0.9753	0.9876
0.0352	1.36	4800	0.0304	0.9764	0.9771	0.9767	0.9880
0.0344	1.45	5100	0.0306	0.9778	0.9744	0.9761	0.9880
0.0337	1.54	5400	0.0288	0.9779	0.9769	0.9774	0.9886
0.0328	1.62	5700	0.0284	0.9776	0.9777	0.9776	0.9888
0.0335	1.71	6000	0.0277	0.9783	0.9779	0.9781	0.9887
0.0329	1.79	6300	0.0284	0.9791	0.9752	0.9772	0.9886
0.0328	1.88	6600	0.0292	0.9764	0.9773	0.9768	0.9882
0.0316	1.96	6900	0.0268	0.9785	0.9773	0.9779	0.9890
0.0264	2.05	7200	0.0272	0.9776	0.9803	0.9789	0.9892
0.0269	2.13	7500	0.0274	0.9792	0.9782	0.9787	0.9891
0.027	2.22	7800	0.0291	0.9774	0.9782	0.9778	0.9889
0.0262	2.3	8100	0.0249	0.9809	0.9807	0.9808	0.9902
0.0258	2.39	8400	0.0255	0.9808	0.9805	0.9806	0.9900
0.0261	2.47	8700	0.0251	0.9808	0.9800	0.9804	0.9900
0.0251	2.56	9000	0.0250	0.9814	0.9788	0.9801	0.9901
0.0248	2.64	9300	0.0248	0.9813	0.9791	0.9802	0.9901
0.0246	2.73	9600	0.0248	0.9800	0.9817	0.9809	0.9902
0.0243	2.82	9900	0.0239	0.9793	0.9819	0.9806	0.9900
0.0241	2.9	10200	0.0236	0.9805	0.9823	0.9814	0.9904
0.0238	2.99	10500	0.0231	0.9822	0.9799	0.9811	0.9907
0.0187	3.07	10800	0.0259	0.9782	0.9823	0.9802	0.9901
0.0188	3.16	11100	0.0231	0.9821	0.9827	0.9824	0.9909
0.0189	3.24	11400	0.0229	0.9830	0.9802	0.9816	0.9910
0.0191	3.33	11700	0.0220	0.9815	0.9827	0.9821	0.9910
0.0187	3.41	12000	0.0223	0.9821	0.9834	0.9828	0.9912
0.018	3.5	12300	0.0224	0.9802	0.9829	0.9815	0.9909
0.0183	3.58	12600	0.0217	0.9823	0.9831	0.9827	0.9911
0.0176	3.67	12900	0.0214	0.9840	0.9824	0.9832	0.9916
0.0177	3.75	13200	0.0211	0.9837	0.9834	0.9835	0.9916
0.0173	3.84	13500	0.0210	0.9828	0.9840	0.9834	0.9916
0.017	3.92	13800	0.0207	0.9832	0.9839	0.9836	0.9916
0.0141	4.01	14100	0.0213	0.9844	0.9838	0.9841	0.9919
0.0129	4.09	14400	0.0213	0.9837	0.9849	0.9843	0.9919
0.013	4.18	14700	0.0228	0.9831	0.9834	0.9833	0.9915
0.0128	4.27	15000	0.0210	0.9844	0.9846	0.9845	0.9920
0.0126	4.35	15300	0.0212	0.9843	0.9842	0.9842	0.9920
0.0125	4.44	15600	0.0214	0.9845	0.9844	0.9844	0.9920
0.0121	4.52	15900	0.0217	0.9844	0.9846	0.9845	0.9921
0.012	4.61	16200	0.0211	0.9847	0.9848	0.9847	0.9922
0.0119	4.69	16500	0.0209	0.9845	0.9852	0.9848	0.9922
0.0116	4.78	16800	0.0211	0.9845	0.9847	0.9846	0.9922
0.0115	4.86	17100	0.0210	0.9850	0.9844	0.9847	0.9923
0.0115	4.95	17400	0.0208	0.9848	0.9853	0.9850	0.9923

Framework versions

Transformers 4.39.1
Pytorch 2.2.0a0+6a974be
Datasets 2.18.0
Tokenizers 0.15.2

adasgaleus/LIM-0.75

20240327211222_nice_straka

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

adasgaleus/
LIM-0.75