t5-small-finetuned-xsum

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

• Loss: 2.2438
• Rouge1: 39.0888
• Rouge2: 16.4223
• Rougel: 39.0782
• Rougelsum: 38.9078
• Gen Len: 12.5217

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• 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	13	2.9857	35.054	9.3338	34.7182	34.6078	12.8261
No log	2.0	26	2.9275	36.982	10.81	36.5767	36.2152	12.2174
No log	3.0	39	2.8723	34.1773	9.1875	33.6848	33.5565	13.1739
No log	4.0	52	2.8198	35.5137	9.1875	34.9118	34.881	12.3043
No log	5.0	65	2.7754	34.8751	9.1875	34.2983	34.1915	12.4348
No log	6.0	78	2.7416	33.8933	9.72	33.9317	33.7063	12.5217
No log	7.0	91	2.7116	35.2289	9.5122	35.285	35.2747	13.0
No log	8.0	104	2.6810	34.0577	8.5778	34.055	34.0948	13.3913
No log	9.0	117	2.6567	32.9647	8.0047	32.9981	33.0779	13.9565
No log	10.0	130	2.6316	32.6956	8.4205	32.7352	32.6825	14.0435
No log	11.0	143	2.6114	32.9656	8.7371	33.0318	33.0445	14.6087
No log	12.0	156	2.5918	33.1517	8.7584	33.1374	33.2133	14.2174
No log	13.0	169	2.5751	34.4539	9.3237	34.4496	34.4093	13.3043
No log	14.0	182	2.5601	33.1915	9.4081	33.3879	33.1639	14.8696
No log	15.0	195	2.5445	33.1915	9.4081	33.3879	33.1639	14.8696
No log	16.0	208	2.5266	33.7765	9.3237	33.8297	33.7816	14.4348
No log	17.0	221	2.5162	35.5923	9.3237	35.5421	35.701	13.5217
No log	18.0	234	2.5053	35.9508	9.2685	35.9326	36.0466	13.0435
No log	19.0	247	2.4925	34.5022	9.3237	34.4987	34.5612	14.0435
No log	20.0	260	2.4797	33.6928	8.834	33.7552	33.8446	14.2609
No log	21.0	273	2.4650	34.1559	8.834	34.2838	34.2381	14.6957
No log	22.0	286	2.4544	36.3845	11.2985	36.4183	36.4242	13.6957
No log	23.0	299	2.4436	35.3914	10.6288	35.2846	35.38	13.6522
No log	24.0	312	2.4311	34.0662	10.1449	34.0753	34.1617	14.3043
No log	25.0	325	2.4245	35.8294	10.3069	35.8467	35.863	12.6957
No log	26.0	338	2.4154	35.4974	10.3069	35.4581	35.531	13.2174
No log	27.0	351	2.4071	34.7645	9.9172	34.8136	34.714	12.913
No log	28.0	364	2.3971	37.3155	13.3583	37.4269	37.4838	12.3913
No log	29.0	377	2.3978	36.9809	13.3583	37.1434	37.1564	12.913
No log	30.0	390	2.3977	35.7727	12.9485	35.9758	35.8481	13.3478
No log	31.0	403	2.3889	36.9445	15.3623	37.0191	36.7931	13.6957
No log	32.0	416	2.3791	36.9312	15.3623	37.0096	36.7726	13.7826
No log	33.0	429	2.3698	38.0341	15.3623	38.1607	37.8796	13.6957
No log	34.0	442	2.3566	38.0813	15.3623	38.2011	37.9199	13.4348
No log	35.0	455	2.3508	38.8091	15.5572	39.0225	38.6247	12.8261
No log	36.0	468	2.3421	38.1147	16.2361	38.2251	37.947	13.7826
No log	37.0	481	2.3368	38.6438	15.5572	38.8191	38.5675	13.0435
No log	38.0	494	2.3402	38.7863	15.9985	38.9217	38.543	13.0
2.6022	39.0	507	2.3356	38.9191	15.9985	39.1569	38.6792	12.6957
2.6022	40.0	520	2.3275	37.4775	15.2311	37.5157	37.3333	13.0435
2.6022	41.0	533	2.3233	38.1397	15.5276	38.2189	38.0466	12.6087
2.6022	42.0	546	2.3179	38.8484	16.743	38.8904	38.6264	12.4348
2.6022	43.0	559	2.3129	38.6908	16.743	38.751	38.5224	12.7391
2.6022	44.0	572	2.3033	38.9067	17.0916	38.8757	38.6766	12.913
2.6022	45.0	585	2.2955	37.9908	16.3768	38.0649	37.9065	13.1304
2.6022	46.0	598	2.2905	37.5099	16.3599	37.575	37.3251	13.3913
2.6022	47.0	611	2.2858	37.9779	16.3599	38.0428	37.861	13.3043
2.6022	48.0	624	2.2846	37.9779	16.3599	38.0428	37.861	13.3043
2.6022	49.0	637	2.2801	39.738	17.3604	39.8135	39.4361	12.5652
2.6022	50.0	650	2.2778	39.738	17.3604	39.8135	39.4361	12.5652
2.6022	51.0	663	2.2787	39.4204	17.2625	39.6063	39.1539	12.6957
2.6022	52.0	676	2.2742	40.4777	16.9406	40.6081	40.2489	12.4348
2.6022	53.0	689	2.2710	37.833	16.1811	37.919	37.8445	13.0435
2.6022	54.0	702	2.2670	37.4441	15.9708	37.5638	37.4827	13.3043
2.6022	55.0	715	2.2680	38.3007	16.4741	38.2323	38.0907	13.6957
2.6022	56.0	728	2.2662	37.7769	16.1816	37.786	37.6852	13.5217
2.6022	57.0	741	2.2657	37.855	16.1816	37.8885	37.8099	13.2174
2.6022	58.0	754	2.2631	37.7579	16.1816	37.8278	37.7224	13.3043
2.6022	59.0	767	2.2626	37.7579	16.1816	37.8278	37.7224	13.3043
2.6022	60.0	780	2.2600	39.4733	17.6398	39.195	39.1691	13.0435
2.6022	61.0	793	2.2603	39.4733	17.6398	39.195	39.1691	13.0435
2.6022	62.0	806	2.2592	39.397	17.6398	39.1585	39.1322	13.1304
2.6022	63.0	819	2.2572	39.3316	17.6398	39.0728	39.0356	13.4348
2.6022	64.0	832	2.2555	39.3316	17.6398	39.0728	39.0356	13.4348
2.6022	65.0	845	2.2549	39.3316	17.6398	39.0728	39.0356	13.4348
2.6022	66.0	858	2.2576	39.3566	17.6398	39.1001	39.0755	13.2609
2.6022	67.0	871	2.2570	38.8037	17.6398	38.6503	38.4646	13.087
2.6022	68.0	884	2.2573	38.8037	17.6398	38.6503	38.4646	13.087
2.6022	69.0	897	2.2570	39.3566	17.6398	39.1001	39.0755	13.2609
2.6022	70.0	910	2.2558	39.9086	17.2751	39.7524	39.6725	13.087
2.6022	71.0	923	2.2564	40.3049	17.4384	40.3022	40.1013	12.913
2.6022	72.0	936	2.2577	39.316	17.6398	39.0515	38.975	13.3478
2.6022	73.0	949	2.2571	39.3566	17.6398	39.1001	39.0755	13.2609
2.6022	74.0	962	2.2545	39.3566	17.6398	39.1001	39.0755	13.2609
2.6022	75.0	975	2.2526	38.8037	17.6398	38.6503	38.4646	13.087
2.6022	76.0	988	2.2519	39.3566	17.6398	39.1001	39.0755	13.2609
2.0634	77.0	1001	2.2514	38.6902	16.4479	38.1227	37.9513	13.1739
2.0634	78.0	1014	2.2503	38.6012	16.4479	38.0664	37.8945	13.2609
2.0634	79.0	1027	2.2489	38.6012	16.4479	38.0664	37.8945	13.2609
2.0634	80.0	1040	2.2473	38.6012	16.4479	38.0664	37.8945	13.2609
2.0634	81.0	1053	2.2454	38.1692	16.4479	37.6072	37.2747	13.087
2.0634	82.0	1066	2.2442	38.1692	16.4479	37.6072	37.2747	13.087
2.0634	83.0	1079	2.2448	38.4097	16.4479	38.3716	38.1151	13.087
2.0634	84.0	1092	2.2451	38.6179	16.4479	38.4637	38.2782	13.2609
2.0634	85.0	1105	2.2453	38.6419	16.4479	38.5054	38.3152	13.087
2.0634	86.0	1118	2.2450	38.9217	15.5035	38.8622	38.6592	12.8261
2.0634	87.0	1131	2.2447	38.9217	15.5035	38.8622	38.6592	12.8261
2.0634	88.0	1144	2.2449	38.9217	15.5035	38.8622	38.6592	12.8261
2.0634	89.0	1157	2.2446	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	90.0	1170	2.2448	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	91.0	1183	2.2449	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	92.0	1196	2.2445	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	93.0	1209	2.2442	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	94.0	1222	2.2439	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	95.0	1235	2.2443	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	96.0	1248	2.2442	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	97.0	1261	2.2439	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	98.0	1274	2.2440	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	99.0	1287	2.2437	39.0888	16.4223	39.0782	38.9078	12.5217
2.0634	100.0	1300	2.2438	39.0888	16.4223	39.0782	38.9078	12.5217

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.0
• Tokenizers 0.13.3