my_awesome_billsum_model

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

• Loss: 2.4894
• Rouge1: 15.1559
• Rouge2: 5.226
• Rougel: 12.2378
• Rougelsum: 12.2239
• Gen Len: 2000.0

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
4.8246	0.0323	2	4.6334	14.4855	5.0231	12.1368	12.1254	2000.0
4.906	0.0645	4	4.5100	14.4329	4.9592	12.0945	12.1106	2000.0
4.8877	0.0968	6	4.3949	14.4597	4.881	12.1046	12.118	2000.0
4.7623	0.1290	8	4.1999	14.3667	4.8653	12.0412	12.0458	2000.0
4.5735	0.1613	10	4.0610	14.4551	4.8268	12.0111	12.029	2000.0
4.1697	0.1935	12	3.9348	14.459	4.8835	12.0186	12.0317	2000.0
3.9466	0.2258	14	3.7285	14.492	4.7994	12.0043	12.0001	2000.0
4.19	0.2581	16	3.6092	14.2935	4.6462	11.8568	11.8758	2000.0
3.7991	0.2903	18	3.5140	14.106	4.4777	11.7222	11.7161	2000.0
3.6421	0.3226	20	3.4145	14.0312	4.4049	11.6747	11.6684	2000.0
3.6484	0.3548	22	3.3426	14.1155	4.4771	11.7146	11.7067	2000.0
3.7566	0.3871	24	3.2824	14.0439	4.4101	11.6456	11.6398	2000.0
3.828	0.4194	26	3.2191	13.9501	4.3142	11.5649	11.558	2000.0
3.505	0.4516	28	3.1688	13.9209	4.2759	11.5698	11.5551	2000.0
3.467	0.4839	30	3.1304	13.816	4.1888	11.4883	11.4751	2000.0
3.2724	0.5161	32	3.0968	13.8275	4.1757	11.4918	11.4765	2000.0
3.1572	0.5484	34	3.0638	13.7565	4.1544	11.4169	11.4017	2000.0
3.3082	0.5806	36	3.0362	13.7676	4.1888	11.4009	11.3806	2000.0
3.2159	0.6129	38	3.0100	13.564	4.0787	11.2744	11.2455	2000.0
3.3438	0.6452	40	2.9825	13.4738	4.0005	11.1618	11.135	2000.0
3.2587	0.6774	42	2.9580	13.4158	4.0646	11.1112	11.0963	2000.0
3.0484	0.7097	44	2.9355	13.3027	4.0331	11.1227	11.1127	2000.0
3.1701	0.7419	46	2.9146	13.3935	4.035	11.1089	11.0943	2000.0
3.1144	0.7742	48	2.8945	13.2357	3.8709	10.9883	10.9698	2000.0
3.2611	0.8065	50	2.8756	13.3365	3.9707	11.0484	11.0462	2000.0
3.0423	0.8387	52	2.8575	13.3542	3.9953	11.0888	11.0779	2000.0
3.1193	0.8710	54	2.8405	13.3056	3.9113	11.118	11.099	2000.0
2.9974	0.9032	56	2.8248	13.3675	3.9264	11.1316	11.1148	2000.0
3.0579	0.9355	58	2.8102	13.371	3.945	11.1374	11.1279	2000.0
3.2434	0.9677	60	2.7964	13.1714	3.871	11.0051	10.9954	2000.0
2.9767	1.0	62	2.7832	13.0728	3.8094	10.9181	10.9149	2000.0
2.9854	1.0323	64	2.7704	12.9766	3.7579	10.8101	10.8082	2000.0
2.8919	1.0645	66	2.7586	13.0417	3.7537	10.824	10.8201	2000.0
2.9225	1.0968	68	2.7472	13.1607	3.8843	10.9298	10.9228	2000.0
3.173	1.1290	70	2.7363	13.0887	3.9032	10.8716	10.8608	2000.0
3.0448	1.1613	72	2.7258	13.1113	3.8846	10.8509	10.8413	2000.0
3.0989	1.1935	74	2.7156	13.2044	3.9782	10.9448	10.9398	2000.0
3.0072	1.2258	76	2.7057	13.272	4.0363	11.0001	10.9965	2000.0
2.7462	1.2581	78	2.6968	13.284	4.0337	10.9831	10.9815	2000.0
3.0383	1.2903	80	2.6879	13.3569	4.0058	10.9546	10.9505	2000.0
3.1326	1.3226	82	2.6793	13.4761	4.1349	11.0978	11.0816	2000.0
2.9859	1.3548	84	2.6710	13.3568	4.1278	11.0238	11.0165	2000.0
2.8721	1.3871	86	2.6630	13.321	4.1405	10.9662	10.9656	2000.0
2.996	1.4194	88	2.6555	13.4558	4.187	11.0321	11.0208	2000.0
2.9725	1.4516	90	2.6484	13.4779	4.1527	11.0813	11.0645	2000.0
3.0609	1.4839	92	2.6416	13.4159	4.1525	11.0169	11.0159	2000.0
2.7738	1.5161	94	2.6351	13.5566	4.2041	11.1207	11.1094	2000.0
2.9562	1.5484	96	2.6290	13.6845	4.313	11.2173	11.201	2000.0
2.6523	1.5806	98	2.6231	13.7239	4.3225	11.2591	11.2495	2000.0
3.0343	1.6129	100	2.6174	13.7076	4.2742	11.2433	11.2304	2000.0
2.7485	1.6452	102	2.6121	13.7974	4.3356	11.2775	11.2672	2000.0
2.9437	1.6774	104	2.6069	13.7932	4.3368	11.3156	11.2995	2000.0
2.8865	1.7097	106	2.6018	13.7692	4.3153	11.2896	11.27	2000.0
2.9826	1.7419	108	2.5967	13.8606	4.3539	11.3807	11.3579	2000.0
2.8272	1.7742	110	2.5918	13.8233	4.3525	11.3732	11.3524	2000.0
2.7165	1.8065	112	2.5874	13.7949	4.3456	11.3495	11.3293	2000.0
2.9133	1.8387	114	2.5833	13.7697	4.2713	11.2912	11.2696	2000.0
2.8366	1.8710	116	2.5795	13.8202	4.3674	11.366	11.3487	2000.0
2.8033	1.9032	118	2.5760	13.8181	4.4343	11.3883	11.3739	2000.0
2.8846	1.9355	120	2.5723	13.7795	4.368	11.3212	11.3145	2000.0
3.0411	1.9677	122	2.5688	13.7885	4.3801	11.3358	11.325	2000.0
2.931	2.0	124	2.5654	13.8741	4.3871	11.3962	11.3926	2000.0
2.7692	2.0323	126	2.5619	13.9234	4.3635	11.4122	11.4131	2000.0
2.576	2.0645	128	2.5588	14.0455	4.3772	11.4421	11.4408	2000.0
2.9965	2.0968	130	2.5559	14.1379	4.4182	11.5059	11.4938	2000.0
2.7233	2.1290	132	2.5532	14.1848	4.3899	11.5132	11.5076	2000.0
2.7718	2.1613	134	2.5507	14.2975	4.4565	11.5842	11.5739	2000.0
2.7089	2.1935	136	2.5482	14.3484	4.5523	11.6193	11.6119	2000.0
2.9317	2.2258	138	2.5457	14.3306	4.5679	11.5783	11.581	2000.0
2.8748	2.2581	140	2.5432	14.354	4.6003	11.6214	11.6195	2000.0
2.9315	2.2903	142	2.5407	14.4648	4.6567	11.7028	11.6888	2000.0
2.7498	2.3226	144	2.5383	14.5232	4.7442	11.7706	11.7634	2000.0
2.9018	2.3548	146	2.5358	14.5162	4.7371	11.7518	11.7461	2000.0
2.8626	2.3871	148	2.5332	14.5341	4.7496	11.7407	11.7339	2000.0
2.8584	2.4194	150	2.5309	14.5072	4.7626	11.7506	11.7441	2000.0
2.8144	2.4516	152	2.5288	14.5934	4.8165	11.7748	11.7664	2000.0
2.9953	2.4839	154	2.5268	14.6244	4.8584	11.8037	11.7946	2000.0
2.8001	2.5161	156	2.5249	14.6272	4.8834	11.798	11.7867	2000.0
2.9155	2.5484	158	2.5232	14.5808	4.8743	11.784	11.7721	2000.0
2.8051	2.5806	160	2.5215	14.6371	4.9178	11.8453	11.8353	2000.0
2.5662	2.6129	162	2.5199	14.6974	4.9668	11.8859	11.8727	2000.0
2.6469	2.6452	164	2.5184	14.6868	4.9259	11.8825	11.865	2000.0
2.8197	2.6774	166	2.5169	14.7867	4.9884	11.9872	11.9718	2000.0
2.5777	2.7097	168	2.5155	14.8429	5.0189	12.0169	12.0112	2000.0
2.8761	2.7419	170	2.5141	14.7896	4.9689	11.9929	11.9731	2000.0
2.5811	2.7742	172	2.5128	14.8042	4.9854	12.0156	11.9908	2000.0
2.7054	2.8065	174	2.5116	14.7848	4.9706	11.9896	11.9707	2000.0
3.0032	2.8387	176	2.5105	14.7583	4.9384	11.9507	11.9375	2000.0
2.7478	2.8710	178	2.5093	14.7583	4.9384	11.9507	11.9375	2000.0
2.9108	2.9032	180	2.5083	14.7757	4.9641	11.9403	11.9253	2000.0
2.6513	2.9355	182	2.5072	14.7844	4.9922	11.974	11.9511	2000.0
2.8323	2.9677	184	2.5061	14.7482	4.9533	11.9389	11.9192	2000.0
2.8963	3.0	186	2.5051	14.8324	5.0133	11.9974	11.9702	2000.0
2.815	3.0323	188	2.5041	14.8624	5.0289	12.0094	11.982	2000.0
2.9109	3.0645	190	2.5030	14.8735	5.0289	12.0258	11.995	2000.0
2.6712	3.0968	192	2.5021	14.9826	5.0544	12.088	12.0656	2000.0
2.6606	3.1290	194	2.5011	14.9826	5.0544	12.088	12.0656	2000.0
2.7432	3.1613	196	2.5002	14.9826	5.0544	12.088	12.0656	2000.0
2.9712	3.1935	198	2.4992	14.9826	5.0544	12.088	12.0656	2000.0
2.6893	3.2258	200	2.4985	14.9696	5.0281	12.0609	12.0404	2000.0
2.8161	3.2581	202	2.4977	14.9196	4.9833	12.0323	12.0162	2000.0
3.1472	3.2903	204	2.4969	14.9196	4.9833	12.0323	12.0162	2000.0
2.5583	3.3226	206	2.4963	14.9173	4.9915	12.0334	12.0144	2000.0
2.7874	3.3548	208	2.4956	14.9874	5.02	12.1013	12.0778	2000.0
2.6359	3.3871	210	2.4950	15.0208	5.0521	12.116	12.0974	2000.0
2.8058	3.4194	212	2.4945	14.9932	5.0521	12.0931	12.074	2000.0
2.6235	3.4516	214	2.4939	15.0197	5.0646	12.1154	12.0984	2000.0
2.6428	3.4839	216	2.4934	15.0643	5.1251	12.1614	12.146	2000.0
2.6676	3.5161	218	2.4929	15.0791	5.1583	12.1771	12.1619	2000.0
2.5883	3.5484	220	2.4925	15.099	5.1968	12.194	12.1806	2000.0
2.9245	3.5806	222	2.4921	15.0971	5.1976	12.2001	12.1868	2000.0
2.9351	3.6129	224	2.4917	15.0971	5.1976	12.2001	12.1868	2000.0
2.9175	3.6452	226	2.4913	15.0966	5.1916	12.1845	12.1757	2000.0
2.6997	3.6774	228	2.4910	15.0851	5.1622	12.1822	12.1717	2000.0
2.7747	3.7097	230	2.4907	15.0803	5.1485	12.1655	12.1563	2000.0
2.5892	3.7419	232	2.4904	15.0803	5.1485	12.1655	12.1563	2000.0
2.7554	3.7742	234	2.4902	15.0604	5.1485	12.1559	12.1488	2000.0
2.8548	3.8065	236	2.4900	15.1559	5.226	12.2378	12.2239	2000.0
2.7879	3.8387	238	2.4898	15.1559	5.226	12.2378	12.2239	2000.0
2.7142	3.8710	240	2.4896	15.1417	5.2106	12.2284	12.2173	2000.0
2.7282	3.9032	242	2.4895	15.1268	5.2071	12.2203	12.2101	2000.0
2.6589	3.9355	244	2.4894	15.1147	5.1913	12.2199	12.2097	2000.0
2.7158	3.9677	246	2.4894	15.1396	5.226	12.233	12.2144	2000.0
2.7397	4.0	248	2.4894	15.1559	5.226	12.2378	12.2239	2000.0

Framework versions

• Transformers 4.55.0
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.4