Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.4742	0.04	50	1.3501	0.5138	0.1534	0.1802	0.2019
1.3854	0.08	100	1.2962	0.5492	0.1444	0.1464	0.1712
1.2044	0.12	150	1.1191	0.6135	0.1935	0.2802	0.2438
1.1934	0.16	200	1.1296	0.6328	0.2580	0.3115	0.2543
1.0589	0.2	250	0.9550	0.6909	0.3103	0.4154	0.3417
1.0216	0.24	300	0.9304	0.7036	0.3720	0.3727	0.3799
0.9372	0.28	350	0.8312	0.7185	0.4050	0.7342	0.3833
0.8818	0.33	400	0.8910	0.7197	0.4773	0.5010	0.4834
0.8957	0.37	450	0.7688	0.7512	0.4872	0.5636	0.4631
0.8311	0.41	500	0.7380	0.7638	0.5687	0.6187	0.5541
0.7595	0.45	550	0.7932	0.7435	0.5502	0.5931	0.5648
0.7677	0.49	600	0.7167	0.7746	0.5999	0.6017	0.6063
0.7386	0.53	650	0.6960	0.7776	0.5608	0.6716	0.5294
0.7731	0.57	700	0.6524	0.7973	0.6137	0.6762	0.5856
0.6949	0.61	750	0.6898	0.7880	0.6079	0.6804	0.5660
0.6982	0.65	800	0.6676	0.7882	0.6021	0.6450	0.5858
0.6805	0.69	850	0.6533	0.7989	0.6216	0.7456	0.6235
0.7633	0.73	900	0.7205	0.7796	0.5835	0.6257	0.6041
0.7712	0.77	950	0.7247	0.7838	0.5740	0.6818	0.5463
0.6768	0.81	1000	0.6328	0.8051	0.6448	0.7470	0.6220
0.671	0.85	1050	0.7261	0.7767	0.5529	0.6892	0.5497
0.6413	0.89	1100	0.6102	0.8100	0.6359	0.6886	0.6147
0.6398	0.93	1150	0.6881	0.7857	0.5860	0.8209	0.5796
0.6588	0.98	1200	0.6264	0.8056	0.6416	0.6564	0.6405
0.5952	1.02	1250	0.6763	0.8119	0.6407	0.6848	0.6231
0.5342	1.06	1300	0.7901	0.7930	0.5880	0.6963	0.5642
0.5187	1.1	1350	0.6499	0.8073	0.6686	0.7048	0.6669
0.5655	1.14	1400	0.6369	0.8061	0.6759	0.6753	0.6796
0.5522	1.18	1450	0.6168	0.8089	0.6496	0.6933	0.6619
0.5308	1.22	1500	0.6293	0.8173	0.6627	0.7965	0.6479
0.628	1.26	1550	0.6275	0.8086	0.6672	0.7533	0.6413
0.4993	1.3	1600	0.6286	0.8150	0.6753	0.7726	0.6521
0.5557	1.34	1650	0.6392	0.8145	0.6380	0.7942	0.6101
0.5315	1.38	1700	0.6072	0.8222	0.6863	0.7386	0.6572
0.5766	1.42	1750	0.6300	0.8120	0.6318	0.8268	0.6121
0.5225	1.46	1800	0.5962	0.8195	0.6903	0.7529	0.6648
0.5074	1.5	1850	0.6217	0.8196	0.6622	0.7711	0.6262
0.5613	1.54	1900	0.5924	0.8246	0.7053	0.7634	0.6756
0.5097	1.59	1950	0.5728	0.8233	0.6791	0.7823	0.6391
0.5001	1.63	2000	0.5828	0.8300	0.7151	0.7483	0.6918
0.5144	1.67	2050	0.5746	0.8256	0.6997	0.7606	0.6727
0.5462	1.71	2100	0.5792	0.8229	0.6932	0.7236	0.6943
0.5252	1.75	2150	0.5827	0.8266	0.6926	0.7896	0.6572
0.5369	1.79	2200	0.6034	0.8142	0.6867	0.7556	0.6558
0.5144	1.83	2250	0.5748	0.8280	0.7103	0.7445	0.6937
0.545	1.87	2300	0.5671	0.8243	0.6942	0.7573	0.6910
0.5151	1.91	2350	0.5685	0.8292	0.6961	0.7770	0.6678
0.5268	1.95	2400	0.5470	0.8318	0.7171	0.7650	0.6974
0.509	1.99	2450	0.5448	0.8336	0.7126	0.7736	0.6885
0.4062	2.03	2500	0.6064	0.8329	0.6949	0.7580	0.6716
0.452	2.07	2550	0.5852	0.8291	0.7058	0.7678	0.6852
0.488	2.11	2600	0.5741	0.8283	0.6993	0.7521	0.6897
0.4459	2.15	2650	0.5606	0.8319	0.7094	0.7706	0.6829
0.4588	2.2	2700	0.5834	0.8253	0.7106	0.7520	0.6914
0.4325	2.24	2750	0.5672	0.8299	0.7149	0.7590	0.6895
0.4182	2.28	2800	0.5661	0.8316	0.7190	0.7527	0.7071
0.4524	2.32	2850	0.5719	0.8329	0.7176	0.7715	0.6936
0.4078	2.36	2900	0.5574	0.8308	0.7149	0.7479	0.7035
0.3654	2.4	2950	0.5658	0.8353	0.7188	0.7521	0.7002
0.4095	2.44	3000	0.5608	0.8335	0.7213	0.7524	0.7019
0.379	2.48	3050	0.5666	0.8365	0.7211	0.7739	0.6949
0.3939	2.52	3100	0.5711	0.8296	0.7203	0.7621	0.6954
0.4039	2.56	3150	0.5748	0.8341	0.7213	0.7641	0.6942
0.4034	2.6	3200	0.5533	0.8348	0.7282	0.7593	0.7065
0.4412	2.64	3250	0.5490	0.8357	0.7250	0.7805	0.6944
0.386	2.68	3300	0.5675	0.8353	0.7296	0.7605	0.7093
0.4298	2.72	3350	0.5525	0.8344	0.7320	0.7583	0.7140
0.384	2.76	3400	0.5629	0.8355	0.7240	0.7734	0.7004
0.3909	2.8	3450	0.5586	0.8344	0.7269	0.7562	0.7132
0.3975	2.85	3500	0.5538	0.8356	0.7253	0.7679	0.7022
0.3906	2.89	3550	0.5566	0.8332	0.7246	0.7570	0.7091
0.3707	2.93	3600	0.5575	0.8359	0.7290	0.7619	0.7095
0.3995	2.97	3650	0.5529	0.8345	0.7296	0.7563	0.7131

Framework versions

Transformers 4.38.2
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.2

waelChafei/RobertaTuned

TTC4900Model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

waelChafei/
RobertaTuned