finetune-longt5

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.9551
• Rouge1 Precision: 0.2602
• Rouge1 Recall: 0.3322
• Rouge1 Fmeasure: 0.2861

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1 Fmeasure	Rouge1 Precision	Rouge1 Recall
2.5865	0.03	10	2.4019	0.2349	0.2481	0.2353
2.3384	0.06	20	2.2294	0.2404	0.2571	0.2385
2.0876	0.1	30	2.2887	0.2467	0.2653	0.2432
2.1205	0.13	40	2.2194	0.2517	0.2731	0.246
2.0637	0.16	50	2.2172	0.2577	0.2868	0.246
1.8623	0.19	60	2.2273	0.2613	0.2903	0.2497
0.0	0.22	70	nan	0.262	0.2911	0.25
1.8591	0.26	80	2.1895	0.2604	0.2901	0.2481
1.9819	0.29	90	2.1492	0.2663	0.2917	0.2575
1.9326	0.32	100	2.1248	0.2698	0.2964	0.2608
1.888	0.35	110	2.1253	0.2698	0.2954	0.2614
1.8934	0.38	120	2.0993	0.2705	0.3006	0.2589
1.8357	0.42	130	2.1050	0.2744	0.3001	0.267
1.8061	0.45	140	2.0705	0.2787	0.2924	0.2829
1.8444	0.48	150	2.1156	0.2739	0.2895	0.2762
1.83	0.51	160	2.0636	0.2773	0.2895	0.2831
1.7433	0.54	170	2.0857	0.2767	0.2811	0.29
1.7523	0.58	180	2.0809	0.2766	0.2798	0.2913
1.764	0.61	190	2.0351	0.2799	0.2751	0.3041
1.8441	0.64	200	2.0460	0.2804	0.2772	0.3025
1.7118	0.67	210	2.0319	0.2798	0.2767	0.3024
1.818	0.7	220	2.0287	0.2823	0.2666	0.318
1.7484	0.74	230	2.0084	0.2822	0.2653	0.3188
1.8224	0.77	240	2.0372	0.2787	0.2634	0.3132
1.6475	0.4	250	2.0281	0.2768	0.2594	0.3141
1.6915	0.42	260	1.9941	0.2851	0.2637	0.3269
1.7106	0.43	270	2.0204	0.2862	0.2718	0.3198
1.6382	0.45	280	2.0073	0.288	0.2657	0.3307
1.6017	0.46	290	2.0242	0.2847	0.2584	0.3328
1.8248	0.48	300	1.9996	0.2821	0.2595	0.3249
1.687	0.5	310	1.9801	0.2857	0.2664	0.3243
1.6993	0.51	320	1.9979	0.2837	0.2608	0.3271
1.6191	0.53	330	2.0025	0.285	0.2618	0.329
1.5409	0.54	340	1.9968	0.2851	0.2616	0.3293
1.6279	0.56	350	1.9940	0.2824	0.2601	0.3259
1.7538	0.58	360	1.9907	0.2803	0.2574	0.3234
1.6781	1.19	370	1.9684	0.2805	0.2565	0.3249
1.6106	1.22	380	1.9798	0.2842	0.2584	0.3314
1.5798	1.25	390	1.9940	0.2842	0.2581	0.3321
1.7134	1.28	400	1.9634	0.2851	0.2596	0.3313
1.6301	1.31	410	1.9644	0.2866	0.2606	0.3342
1.6657	1.35	420	1.9775	0.2861	0.2604	0.3325
1.7028	1.38	430	1.9792	0.2838	0.26	0.3275
1.7063	1.41	440	1.9728	0.2838	0.2598	0.3279
1.5825	1.44	450	1.9644	0.2838	0.259	0.3291
1.5955	1.47	460	1.9674	0.2864	0.2605	0.3329
1.6774	1.51	470	1.9718	0.288	0.2616	0.3354
1.7171	1.54	480	1.9602	0.2882	0.2619	0.3358
1.6631	1.57	490	1.9592	0.2873	0.2614	0.3335
1.5405	1.6	500	1.9625	0.2868	0.261	0.3329
1.5711	1.63	510	1.9690	0.2872	0.2614	0.3337
1.7382	1.67	520	1.9669	0.2873	0.262	0.3326
1.6345	1.7	530	1.9564	0.2867	0.2615	0.3323
1.6289	1.73	540	1.9558	0.2856	0.2604	0.3309
1.575	1.76	550	1.9620	0.2872	0.2616	0.333
1.682	1.79	560	1.9613	0.287	0.2615	0.3326
1.5385	1.83	570	1.9616	0.2869	0.2614	0.3324
1.62	1.86	580	1.9603	0.2868	0.2611	0.3325
1.6026	1.89	590	1.9589	0.2867	0.2611	0.3325
1.6511	1.92	600	1.9575	0.2865	0.2608	0.3322
1.5688	1.95	610	1.9555	0.2866	0.2608	0.3327
1.6092	1.99	620	1.9553	0.2863	0.2604	0.3324

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.15.1