Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.1456	0.4	200	1.8334	34.4069	14.6208	29.4432	29.4556	18.986
1.9791	0.8	400	1.7728	34.2402	14.6376	29.5349	29.5363	18.986
1.8807	1.2	600	1.7601	34.5439	15.1529	29.8476	29.803	18.986
1.8082	1.6	800	1.7609	34.6764	15.2571	30.0381	29.9641	18.99
1.8193	2.0	1000	1.7262	34.7376	15.0654	29.8906	29.9217	19.0
1.7098	2.4	1200	1.7295	35.1726	15.5835	30.3994	30.4092	18.998
1.702	2.8	1400	1.7391	34.3418	15.311	29.6477	29.6509	19.0
1.6659	3.2	1600	1.7512	34.5394	15.5073	30.1215	30.1061	18.996
1.6216	3.6	1800	1.7485	34.9325	15.5266	30.0803	30.0892	19.0
1.6217	4.0	2000	1.7286	34.8255	15.328	29.7924	29.8133	19.0
1.5645	4.4	2200	1.7570	35.0673	15.3704	29.9302	29.9333	19.0
1.5454	4.8	2400	1.7453	34.7481	15.1067	29.9309	29.873	19.0
1.5012	5.2	2600	1.7661	35.0534	15.4418	30.0802	30.0423	19.0
1.4867	5.6	2800	1.7647	35.5583	15.5157	30.4281	30.4351	19.0
1.4914	6.0	3000	1.7587	35.0749	15.5551	29.9916	30.0154	19.0
1.4307	6.4	3200	1.7922	34.8942	15.248	29.6589	29.6454	19.0
1.432	6.8	3400	1.7761	34.5489	15.0722	29.585	29.5869	19.0
1.4142	7.2	3600	1.7966	34.4441	15.3042	29.8935	29.8639	19.0
1.3605	7.6	3800	1.8043	34.511	15.4382	29.5617	29.564	19.0
1.3846	8.0	4000	1.7951	35.1945	15.5203	29.8749	29.879	19.0
1.3311	8.4	4200	1.8232	35.4176	15.8548	30.4327	30.3956	19.0
1.3329	8.8	4400	1.8223	35.4969	15.6317	30.2432	30.222	19.0
1.3214	9.2	4600	1.8441	35.4614	15.8153	29.9465	29.9406	19.0
1.2979	9.6	4800	1.8392	34.9863	15.5971	30.003	30.0149	19.0
1.2782	10.0	5000	1.8395	35.1628	15.8039	30.1611	30.1363	19.0
1.2708	10.4	5200	1.8701	34.9822	15.5759	29.7916	29.7935	19.0
1.2659	10.8	5400	1.8575	35.3481	15.5923	29.9914	29.9391	19.0
1.2302	11.2	5600	1.8695	35.4173	15.8071	30.3649	30.3185	19.0
1.229	11.6	5800	1.8685	35.2428	15.795	29.9917	29.9481	18.996
1.2185	12.0	6000	1.8837	34.8893	15.6796	29.9493	29.9607	19.0
1.1934	12.4	6200	1.8790	34.8951	15.4768	29.9075	29.9236	19.0
1.1922	12.8	6400	1.9001	35.1576	15.7902	29.9736	29.9386	19.0
1.2022	13.2	6600	1.8984	34.7087	15.3546	29.5658	29.5201	18.998
1.173	13.6	6800	1.9125	34.9104	15.3827	29.7326	29.7116	19.0
1.1873	14.0	7000	1.8984	35.3422	15.639	30.1525	30.1138	19.0
1.1561	14.4	7200	1.9096	35.2554	15.747	30.0491	29.9829	19.0
1.154	14.8	7400	1.9043	35.0523	15.5168	29.974	29.9442	18.992
1.1351	15.2	7600	1.9202	35.2258	15.8262	30.4862	30.4702	18.996
1.1528	15.6	7800	1.9253	35.2228	15.7183	30.1423	30.1282	18.996
1.1413	16.0	8000	1.9251	35.2414	15.5881	30.0468	30.0212	18.998
1.1375	16.4	8200	1.9256	34.8926	15.451	29.8252	29.8018	18.992
1.1025	16.8	8400	1.9359	35.3022	15.7612	30.0581	30.0105	19.0
1.1045	17.2	8600	1.9452	35.2059	15.7584	30.0534	30.0004	19.0
1.1052	17.6	8800	1.9353	35.2928	15.7749	30.1623	30.1519	19.0
1.1261	18.0	9000	1.9412	35.5988	16.0219	30.4918	30.4493	19.0
1.1044	18.4	9200	1.9454	35.4386	15.7284	30.2409	30.194	19.0
1.0999	18.8	9400	1.9449	35.2472	15.7148	30.1136	30.119	18.992
1.1044	19.2	9600	1.9466	35.2895	15.7895	30.1843	30.1468	19.0
1.1021	19.6	9800	1.9474	35.2796	15.7082	30.0929	30.0662	19.0
1.0954	20.0	10000	1.9488	35.319	15.7079	30.1428	30.1129	19.0

Framework versions

Transformers 4.18.0
Pytorch 1.10.0+cu111
Datasets 2.5.1
Tokenizers 0.12.1

Zekunli/flan-t5-large-extraction-cnndm_4000-all-ep20

flan-t5-large-extraction-cnndm_4000-all-ep20

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Zekunli/
flan-t5-large-extraction-cnndm_4000-all-ep20