Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	1	4.6285	30.5085	27.5862	30.5085	30.5085	19.0
No log	2.0	2	4.5962	30.5085	27.5862	30.5085	30.5085	19.0
No log	3.0	3	4.5647	30.5085	27.5862	30.5085	30.5085	19.0
No log	4.0	4	4.5332	30.5085	27.5862	30.5085	30.5085	19.0
No log	5.0	5	4.5017	30.5085	27.5862	30.5085	30.5085	19.0
No log	6.0	6	4.4700	30.5085	27.5862	30.5085	30.5085	19.0
No log	7.0	7	4.4392	30.5085	27.5862	30.5085	30.5085	19.0
No log	8.0	8	4.4086	30.5085	27.5862	30.5085	30.5085	19.0
No log	9.0	9	4.3787	30.5085	27.5862	30.5085	30.5085	19.0
No log	10.0	10	4.3466	30.5085	27.5862	30.5085	30.5085	19.0
No log	11.0	11	4.3033	30.5085	27.5862	30.5085	30.5085	19.0
No log	12.0	12	4.2622	30.5085	27.5862	30.5085	30.5085	19.0
No log	13.0	13	4.2329	30.5085	27.5862	30.5085	30.5085	19.0
No log	14.0	14	4.2033	30.5085	27.5862	30.5085	30.5085	19.0
No log	15.0	15	4.1732	30.5085	27.5862	30.5085	30.5085	19.0
No log	16.0	16	4.1432	30.5085	27.5862	30.5085	30.5085	19.0
No log	17.0	17	4.1130	30.5085	27.5862	30.5085	30.5085	19.0
No log	18.0	18	4.0838	30.5085	27.5862	30.5085	30.5085	19.0
No log	19.0	19	4.0550	30.5085	27.5862	30.5085	30.5085	19.0
No log	20.0	20	4.0268	30.5085	27.5862	30.5085	30.5085	19.0
No log	21.0	21	3.9994	31.9328	30.7692	31.9328	31.9328	19.0
No log	22.0	22	3.9723	31.9328	30.7692	31.9328	31.9328	19.0
No log	23.0	23	3.9461	31.9328	30.7692	31.9328	31.9328	19.0
No log	24.0	24	3.9208	31.9328	30.7692	31.9328	31.9328	19.0
No log	25.0	25	3.8969	31.9328	30.7692	31.9328	31.9328	19.0
No log	26.0	26	3.8736	31.9328	30.7692	31.9328	31.9328	19.0
No log	27.0	27	3.8511	31.9328	30.7692	31.9328	31.9328	19.0
No log	28.0	28	3.8275	31.9328	30.7692	31.9328	31.9328	19.0
No log	29.0	29	3.8040	31.9328	30.7692	31.9328	31.9328	19.0
No log	30.0	30	3.7818	31.9328	30.7692	31.9328	31.9328	19.0
No log	31.0	31	3.7597	31.9328	30.7692	31.9328	31.9328	19.0
No log	32.0	32	3.7381	31.9328	30.7692	31.9328	31.9328	19.0
No log	33.0	33	3.7174	31.9328	30.7692	31.9328	31.9328	19.0
No log	34.0	34	3.6983	31.9328	30.7692	31.9328	31.9328	19.0
No log	35.0	35	3.6802	31.9328	30.7692	31.9328	31.9328	19.0
No log	36.0	36	3.6628	31.9328	30.7692	31.9328	31.9328	19.0
No log	37.0	37	3.6461	31.9328	30.7692	31.9328	31.9328	19.0
No log	38.0	38	3.6301	31.9328	30.7692	31.9328	31.9328	19.0
No log	39.0	39	3.6150	31.9328	30.7692	31.9328	31.9328	19.0
No log	40.0	40	3.6005	31.9328	30.7692	31.9328	31.9328	19.0
No log	41.0	41	3.5871	31.9328	30.7692	31.9328	31.9328	19.0
No log	42.0	42	3.5744	31.9328	30.7692	31.9328	31.9328	19.0
No log	43.0	43	3.5628	31.9328	30.7692	31.9328	31.9328	19.0
No log	44.0	44	3.5516	31.9328	30.7692	31.9328	31.9328	19.0
No log	45.0	45	3.5410	31.9328	30.7692	31.9328	31.9328	19.0
No log	46.0	46	3.5308	31.9328	30.7692	31.9328	31.9328	19.0
No log	47.0	47	3.5212	31.9328	30.7692	31.9328	31.9328	19.0
No log	48.0	48	3.5122	31.9328	30.7692	31.9328	31.9328	19.0
No log	49.0	49	3.5039	31.9328	30.7692	31.9328	31.9328	19.0
No log	50.0	50	3.4961	31.9328	30.7692	31.9328	31.9328	19.0
No log	51.0	51	3.4889	31.9328	30.7692	31.9328	31.9328	19.0
No log	52.0	52	3.4818	31.9328	30.7692	31.9328	31.9328	19.0
No log	53.0	53	3.4745	31.9328	30.7692	31.9328	31.9328	19.0
No log	54.0	54	3.4679	31.9328	30.7692	31.9328	31.9328	19.0
No log	55.0	55	3.4618	31.9328	30.7692	31.9328	31.9328	19.0
No log	56.0	56	3.4557	31.9328	30.7692	31.9328	31.9328	19.0
No log	57.0	57	3.4502	31.9328	30.7692	31.9328	31.9328	19.0
No log	58.0	58	3.4447	31.9328	30.7692	31.9328	31.9328	19.0
No log	59.0	59	3.4391	31.9328	30.7692	31.9328	31.9328	19.0
No log	60.0	60	3.4336	31.9328	30.7692	31.9328	31.9328	19.0
No log	61.0	61	3.4285	31.9328	30.7692	31.9328	31.9328	19.0
No log	62.0	62	3.4234	31.9328	30.7692	31.9328	31.9328	19.0
No log	63.0	63	3.4186	31.9328	30.7692	31.9328	31.9328	19.0
No log	64.0	64	3.4140	31.9328	30.7692	31.9328	31.9328	19.0
No log	65.0	65	3.4094	31.9328	30.7692	31.9328	31.9328	19.0
No log	66.0	66	3.4052	31.9328	30.7692	31.9328	31.9328	19.0
No log	67.0	67	3.4014	31.9328	30.7692	31.9328	31.9328	19.0
No log	68.0	68	3.3979	31.9328	30.7692	31.9328	31.9328	19.0
No log	69.0	69	3.3946	31.9328	30.7692	31.9328	31.9328	19.0
No log	70.0	70	3.3914	31.9328	30.7692	31.9328	31.9328	19.0
No log	71.0	71	3.3880	31.9328	30.7692	31.9328	31.9328	19.0
No log	72.0	72	3.3848	31.9328	30.7692	31.9328	31.9328	19.0
No log	73.0	73	3.3818	31.9328	30.7692	31.9328	31.9328	19.0
No log	74.0	74	3.3790	31.9328	30.7692	31.9328	31.9328	19.0
No log	75.0	75	3.3764	31.9328	30.7692	31.9328	31.9328	19.0
No log	76.0	76	3.3742	31.9328	30.7692	31.9328	31.9328	19.0
No log	77.0	77	3.3720	31.9328	30.7692	31.9328	31.9328	19.0
No log	78.0	78	3.3699	31.9328	30.7692	31.9328	31.9328	19.0
No log	79.0	79	3.3680	31.9328	30.7692	31.9328	31.9328	19.0
No log	80.0	80	3.3663	31.9328	30.7692	31.9328	31.9328	19.0
No log	81.0	81	3.3645	31.9328	30.7692	31.9328	31.9328	19.0
No log	82.0	82	3.3629	31.9328	30.7692	31.9328	31.9328	19.0
No log	83.0	83	3.3615	31.9328	30.7692	31.9328	31.9328	19.0
No log	84.0	84	3.3602	31.9328	30.7692	31.9328	31.9328	19.0
No log	85.0	85	3.3591	31.9328	30.7692	31.9328	31.9328	19.0
No log	86.0	86	3.3582	31.9328	30.7692	31.9328	31.9328	19.0
No log	87.0	87	3.3573	31.9328	30.7692	31.9328	31.9328	19.0
No log	88.0	88	3.3565	31.9328	30.7692	31.9328	31.9328	19.0
No log	89.0	89	3.3557	31.9328	30.7692	31.9328	31.9328	19.0
No log	90.0	90	3.3549	31.9328	30.7692	31.9328	31.9328	19.0
No log	91.0	91	3.3543	31.9328	30.7692	31.9328	31.9328	19.0
No log	92.0	92	3.3537	31.9328	30.7692	31.9328	31.9328	19.0
No log	93.0	93	3.3531	31.9328	30.7692	31.9328	31.9328	19.0
No log	94.0	94	3.3525	31.9328	30.7692	31.9328	31.9328	19.0
No log	95.0	95	3.3521	31.9328	30.7692	31.9328	31.9328	19.0
No log	96.0	96	3.3517	31.9328	30.7692	31.9328	31.9328	19.0
No log	97.0	97	3.3515	31.9328	30.7692	31.9328	31.9328	19.0
No log	98.0	98	3.3512	31.9328	30.7692	31.9328	31.9328	19.0
No log	99.0	99	3.3511	31.9328	30.7692	31.9328	31.9328	19.0
No log	100.0	100	3.3510	31.9328	30.7692	31.9328	31.9328	19.0

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

vaibhavchavan/t5-small-finetuned-xsum

t5-small-finetuned-xsum

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vaibhavchavan/
t5-small-finetuned-xsum