fine-tuned-2048-inputs-30-epochs

This model is a fine-tuned version of GanjinZero/biobart-v2-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8273
• Rouge1: 0.2909
• Rouge2: 0.117
• Rougel: 0.2667
• Rougelsum: 0.2666
• Gen Len: 15.53

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	151	0.7529	0.2073	0.0784	0.1893	0.1893	13.16
No log	2.0	302	0.7144	0.2569	0.0828	0.2249	0.2262	13.76
No log	3.0	453	0.6993	0.2397	0.0787	0.2138	0.2143	14.52
0.7226	4.0	604	0.6957	0.2857	0.1014	0.2603	0.2619	14.27
0.7226	5.0	755	0.7037	0.2906	0.1168	0.2653	0.268	14.73
0.7226	6.0	906	0.6971	0.2753	0.1126	0.2512	0.2512	14.92
0.4948	7.0	1057	0.7117	0.2806	0.1139	0.2546	0.2539	14.93
0.4948	8.0	1208	0.7185	0.2931	0.1188	0.2667	0.2685	14.45
0.4948	9.0	1359	0.7250	0.3007	0.1106	0.2736	0.2755	14.76
0.368	10.0	1510	0.7343	0.3157	0.126	0.2908	0.2904	14.67
0.368	11.0	1661	0.7418	0.3045	0.1194	0.2758	0.2757	15.23
0.368	12.0	1812	0.7521	0.2981	0.113	0.2745	0.275	14.91
0.368	13.0	1963	0.7556	0.2902	0.1142	0.2695	0.2712	15.01
0.2865	14.0	2114	0.7636	0.3145	0.1238	0.2936	0.295	15.44
0.2865	15.0	2265	0.7722	0.2965	0.1102	0.2684	0.2704	14.93
0.2865	16.0	2416	0.7788	0.3015	0.1087	0.2737	0.2751	15.29
0.2221	17.0	2567	0.7834	0.2957	0.1127	0.2691	0.2688	15.11
0.2221	18.0	2718	0.7905	0.292	0.1136	0.2595	0.2596	15.1
0.2221	19.0	2869	0.7945	0.2903	0.1027	0.2626	0.263	15.5
0.1825	20.0	3020	0.8033	0.3146	0.1226	0.2826	0.2839	15.54
0.1825	21.0	3171	0.8009	0.3027	0.114	0.2742	0.2749	15.69
0.1825	22.0	3322	0.8085	0.2951	0.1132	0.2616	0.2624	15.37
0.1825	23.0	3473	0.8120	0.3045	0.1182	0.2733	0.2749	15.48
0.1498	24.0	3624	0.8163	0.3015	0.111	0.2723	0.2738	15.47
0.1498	25.0	3775	0.8197	0.3054	0.1144	0.2785	0.2778	15.51
0.1498	26.0	3926	0.8212	0.2987	0.1199	0.2723	0.2709	15.59
0.1329	27.0	4077	0.8230	0.3025	0.1154	0.2751	0.2756	15.5
0.1329	28.0	4228	0.8250	0.2845	0.1108	0.2599	0.2608	15.49
0.1329	29.0	4379	0.8275	0.3002	0.1102	0.2745	0.2753	15.57
0.1226	30.0	4530	0.8273	0.2909	0.117	0.2667	0.2666	15.53

Framework versions

• Transformers 4.36.2
• Pytorch 1.12.1+cu113
• Datasets 2.15.0
• Tokenizers 0.15.0