flan-t5-base-samsum

This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3675
• Rouge1: 47.8828
• Rouge2: 23.7643
• Rougel: 40.0631
• Rougelsum: 43.9278
• Gen Len: 17.8926

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	461	1.3918	47.3769	23.3838	39.3973	43.4564	18.0281
1.4592	2.0	922	1.3709	47.2206	23.1789	39.6014	43.4079	17.9109
1.3784	3.0	1383	1.3713	47.7845	23.8526	40.136	43.9168	17.8657
1.3201	4.0	1844	1.3708	47.6777	23.908	39.9163	43.8836	17.9609
1.2746	5.0	2305	1.3675	47.8828	23.7643	40.0631	43.9278	17.8926
1.2301	6.0	2766	1.3713	48.2323	24.4937	40.5686	44.4202	17.9560
1.1981	7.0	3227	1.3751	47.8381	24.1644	40.2422	44.1291	17.8657
1.1573	8.0	3688	1.3813	48.4652	24.6443	40.6434	44.3745	17.9255
1.1266	9.0	4149	1.3868	47.9121	24.385	40.4372	44.0988	17.9341
1.0957	10.0	4610	1.4023	47.8642	24.159	40.1227	43.7605	17.7009
1.0631	11.0	5071	1.4018	48.2775	24.5049	40.7079	44.2074	17.9328
1.0389	12.0	5532	1.4117	48.3601	24.5203	40.7592	44.274	17.9707
1.0389	13.0	5993	1.4263	48.2238	24.4153	40.5813	44.1677	18.0183
1.0109	14.0	6454	1.4291	48.1956	24.2847	40.4305	44.1961	17.9524
0.9847	15.0	6915	1.4371	48.2127	24.3354	40.6064	44.2341	17.9634
0.9589	16.0	7376	1.4529	47.9105	23.9675	40.1748	43.7047	18.0440
0.9378	17.0	7837	1.4606	48.1865	24.4622	40.569	44.1542	17.9560
0.9099	18.0	8298	1.4772	48.211	24.4775	40.5584	44.1599	17.7924
0.895	19.0	8759	1.4904	48.1122	24.3564	40.3353	43.9564	18.0488
0.8671	20.0	9220	1.5056	47.5799	23.7658	39.9706	43.2975	17.9988
0.8494	21.0	9681	1.5137	48.0903	24.423	40.6774	44.1481	18.0220
0.8331	22.0	10142	1.5297	47.7549	23.9496	39.9768	43.565	18.0366
0.8164	23.0	10603	1.5351	47.8721	24.2145	40.3296	43.785	18.1184
0.7938	24.0	11064	1.5519	47.5822	24.0177	39.9271	43.5449	18.1795
0.776	25.0	11525	1.5629	47.9797	24.4946	40.4895	44.0603	18.1319
0.776	26.0	11986	1.5821	47.739	24.1491	40.245	43.6279	18.0098
0.7605	27.0	12447	1.5956	47.651	23.8254	40.1417	43.6129	18.0317
0.7439	28.0	12908	1.6122	48.0205	24.1036	40.4749	43.9228	17.9744
0.7264	29.0	13369	1.6245	47.8966	24.4286	40.3862	43.8228	18.0891
0.7112	30.0	13830	1.6446	47.7791	24.0747	40.2562	43.7814	18.1306
0.695	31.0	14291	1.6522	47.9603	23.8705	40.3761	43.7575	17.9963
0.6828	32.0	14752	1.6655	47.9677	23.7249	40.1378	43.7374	18.0440
0.6673	33.0	15213	1.6845	47.8355	23.945	40.2317	43.8354	18.0965
0.6576	34.0	15674	1.6959	47.4403	23.6271	39.8488	43.3228	17.9695
0.6435	35.0	16135	1.7059	47.117	23.1881	39.6582	42.9238	17.9451
0.6298	36.0	16596	1.7203	47.3955	23.2996	39.8436	43.2118	18.0305
0.6184	37.0	17057	1.7371	47.562	23.4778	39.7404	43.2463	18.0403
0.6036	38.0	17518	1.7521	47.5431	23.2235	39.8305	43.3672	18.0965
0.6036	39.0	17979	1.7673	47.8626	23.6537	40.1509	43.5389	18.1966
0.5934	40.0	18440	1.7847	47.729	23.3849	39.9981	43.4882	18.1795
0.5805	41.0	18901	1.7984	47.5329	23.5869	39.7265	43.3789	18.2137
0.5701	42.0	19362	1.8035	47.0939	22.9865	39.4487	42.8539	18.0867
0.562	43.0	19823	1.8386	47.228	23.0686	39.5491	42.8498	18.2479
0.5511	44.0	20284	1.8370	47.0404	23.0821	39.5151	42.6788	18.0342
0.5403	45.0	20745	1.8460	47.4276	23.4609	39.8966	43.2549	17.9670
0.5299	46.0	21206	1.8807	47.2177	23.3358	39.5503	42.9442	18.1221
0.5216	47.0	21667	1.8937	47.3416	23.453	39.5797	43.0491	18.2088
0.5136	48.0	22128	1.8858	47.6139	23.4384	39.6112	43.129	18.1477
0.5058	49.0	22589	1.9066	47.6882	23.3361	39.6478	43.1809	18.1880
0.4965	50.0	23050	1.9291	47.2049	22.9388	39.2422	42.8779	18.2198
0.4875	51.0	23511	1.9273	47.3704	22.9981	39.3522	42.9689	18.0659
0.4875	52.0	23972	1.9435	47.2247	22.8976	39.1929	42.8444	18.0696
0.4779	53.0	24433	1.9612	47.2979	23.2517	39.3149	42.9348	18.1575
0.4713	54.0	24894	1.9682	47.1551	22.8543	39.0908	42.832	18.1551
0.4655	55.0	25355	1.9874	47.5333	23.1055	39.5242	43.1433	18.2332
0.4554	56.0	25816	1.9905	47.2574	23.0125	39.401	42.8966	18.1319
0.4512	57.0	26277	2.0082	47.3288	23.0371	39.3762	42.9666	18.0720
0.4456	58.0	26738	2.0122	47.2456	23.1162	39.3492	42.8017	18.2491
0.437	59.0	27199	2.0232	47.0192	22.8747	39.1484	42.7908	18.1233
0.4321	60.0	27660	2.0494	47.263	22.9081	39.3591	43.0016	18.1429
0.4247	61.0	28121	2.0568	46.7482	22.3943	38.7944	42.3775	18.0952
0.4198	62.0	28582	2.0613	47.1438	23.0841	39.3236	42.8207	18.1184
0.4139	63.0	29043	2.0880	47.3858	22.9625	39.3917	42.8971	18.1306
0.4076	64.0	29504	2.0850	47.1677	22.5965	39.1316	42.5938	18.1954
0.4076	65.0	29965	2.1201	47.1502	22.9045	39.1131	42.762	18.2454
0.4016	66.0	30426	2.1195	47.2316	23.2222	39.401	42.9895	18.2686
0.3972	67.0	30887	2.1212	47.4765	23.2306	39.5013	43.1835	18.0977
0.393	68.0	31348	2.1195	47.1038	22.9678	39.2464	42.8203	18.1770
0.388	69.0	31809	2.1542	47.5693	23.2268	39.6862	43.1645	18.1978
0.383	70.0	32270	2.1455	46.8651	22.6649	38.9893	42.4992	18.2332
0.3782	71.0	32731	2.1639	47.1875	22.9234	39.2132	42.7482	18.0977
0.3744	72.0	33192	2.1863	46.8928	22.6297	39.104	42.5924	18.1697
0.3726	73.0	33653	2.1814	47.1538	22.775	39.0752	42.6987	18.0928
0.3663	74.0	34114	2.1895	46.8957	22.6746	39.0239	42.5713	18.1282
0.3634	75.0	34575	2.2111	47.0813	22.9038	39.1347	42.6788	18.1844
0.3609	76.0	35036	2.2134	47.0427	22.8227	39.0936	42.6873	18.2576
0.3609	77.0	35497	2.2141	47.1654	23.0001	39.1324	42.7932	18.1123
0.3564	78.0	35958	2.2352	47.2883	23.0232	39.2823	42.9287	18.1612
0.3518	79.0	36419	2.2386	47.0606	22.805	39.0993	42.6367	18.1600
0.3505	80.0	36880	2.2462	47.295	22.9675	39.3187	42.9416	18.1282
0.3467	81.0	37341	2.2530	47.5325	23.2826	39.5613	43.1435	18.1306
0.3444	82.0	37802	2.2573	47.2323	23.0027	39.4388	42.8436	18.0891
0.3406	83.0	38263	2.2475	47.3953	23.2198	39.4927	42.9521	18.0879
0.3398	84.0	38724	2.2679	47.371	23.1285	39.3458	42.9232	18.0720
0.3369	85.0	39185	2.2771	47.1999	23.1471	39.2632	42.8045	18.1123
0.3364	86.0	39646	2.2726	46.9803	22.7742	39.1762	42.6037	18.1612
0.332	87.0	40107	2.2881	46.9856	22.8314	39.2023	42.6538	18.0672
0.3314	88.0	40568	2.2937	47.2246	22.9515	39.3104	42.8241	18.1722
0.3301	89.0	41029	2.2944	47.0335	23.0095	39.1505	42.75	18.1477
0.3301	90.0	41490	2.3051	47.3389	23.1427	39.2888	42.991	18.1624
0.3268	91.0	41951	2.2996	47.2417	23.095	39.2262	42.8677	18.1819
0.3254	92.0	42412	2.3150	47.1145	22.875	39.0419	42.6853	18.1880
0.3242	93.0	42873	2.3123	47.3604	23.0515	39.3203	42.9265	18.1844
0.3237	94.0	43334	2.3258	47.1804	22.85	39.1514	42.6965	18.1465
0.3214	95.0	43795	2.3255	47.255	22.974	39.1976	42.8042	18.1807
0.3198	96.0	44256	2.3213	47.302	22.9124	39.2406	42.8763	18.1270
0.3196	97.0	44717	2.3311	47.2505	22.9494	39.2155	42.8255	18.1600
0.3199	98.0	45178	2.3252	47.1703	22.91	39.1495	42.7593	18.1368
0.3205	99.0	45639	2.3270	47.2449	22.9545	39.1447	42.8473	18.1966
0.3193	100.0	46100	2.3269	47.2229	22.9034	39.0989	42.7894	18.1990

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.1+cu128
• Datasets 4.4.2
• Tokenizers 0.22.1