Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
No log	1.0	1	7.1720	0.0	0.0	0.0	0.0
No log	2.0	2	6.2006	0.1333	0.0	0.1333	0.1333
No log	3.0	3	5.7428	0.1333	0.0	0.1333	0.1333
No log	4.0	4	3.8803	0.1333	0.0	0.1333	0.1333
No log	5.0	5	5.0966	0.0	0.0	0.0	0.0
No log	6.0	6	5.3543	0.0	0.0	0.0	0.0
No log	7.0	7	5.1282	0.0	0.0	0.0	0.0
No log	8.0	8	4.5391	0.2	0.0	0.2	0.2
No log	9.0	9	3.3062	0.25	0.0	0.25	0.25
No log	10.0	10	2.5865	0.25	0.0	0.25	0.25
No log	11.0	11	2.4655	0.0	0.0	0.0	0.0
No log	12.0	12	2.0948	0.0	0.0	0.0	0.0
No log	13.0	13	1.8355	0.25	0.1429	0.25	0.25
No log	14.0	14	1.5715	0.25	0.1429	0.25	0.25
No log	15.0	15	1.3096	0.3333	0.25	0.3333	0.3333
No log	16.0	16	1.1770	0.2105	0.1176	0.2105	0.2105
No log	17.0	17	1.0978	0.3158	0.2353	0.3158	0.3158
No log	18.0	18	1.2578	0.3158	0.2353	0.3158	0.3158
No log	19.0	19	1.0386	0.3158	0.2353	0.3158	0.3158
No log	20.0	20	0.7758	0.25	0.0	0.25	0.25
No log	21.0	21	0.6506	0.2222	0.125	0.2222	0.2222
No log	22.0	22	0.7023	0.2222	0.125	0.2222	0.2222
No log	23.0	23	0.6491	0.2222	0.125	0.2222	0.2222
No log	24.0	24	0.6289	0.2105	0.1176	0.2105	0.2105
No log	25.0	25	0.6138	0.4444	0.2857	0.4444	0.4444
No log	26.0	26	0.6190	0.4	0.25	0.4	0.4
No log	27.0	27	0.4749	0.2105	0.1176	0.2105	0.2105
No log	28.0	28	0.3589	0.25	0.0	0.25	0.25
No log	29.0	29	0.4597	0.3077	0.0	0.3077	0.3077
No log	30.0	30	0.3665	0.3077	0.0	0.3077	0.3077
No log	31.0	31	0.2642	0.3077	0.0	0.3077	0.3077
No log	32.0	32	0.1914	0.3077	0.0	0.3077	0.3077
No log	33.0	33	0.1114	1.0	1.0	1.0	1.0
No log	34.0	34	0.0375	1.0	1.0	1.0	1.0
No log	35.0	35	0.0187	1.0	1.0	1.0	1.0
No log	36.0	36	0.0111	1.0	1.0	1.0	1.0
No log	37.0	37	0.0046	1.0	1.0	1.0	1.0
No log	38.0	38	0.0021	1.0	1.0	1.0	1.0
No log	39.0	39	0.0012	1.0	1.0	1.0	1.0
No log	40.0	40	0.0007	1.0	1.0	1.0	1.0
No log	41.0	41	0.0004	1.0	1.0	1.0	1.0
No log	42.0	42	0.0005	1.0	1.0	1.0	1.0
No log	43.0	43	0.0007	1.0	1.0	1.0	1.0
No log	44.0	44	0.0009	1.0	1.0	1.0	1.0
No log	45.0	45	0.0016	1.0	1.0	1.0	1.0
No log	46.0	46	0.0008	1.0	1.0	1.0	1.0
No log	47.0	47	0.0002	1.0	1.0	1.0	1.0
No log	48.0	48	0.0000	1.0	1.0	1.0	1.0
No log	49.0	49	0.0000	1.0	1.0	1.0	1.0
No log	50.0	50	0.0000	1.0	1.0	1.0	1.0
No log	51.0	51	0.0000	1.0	1.0	1.0	1.0
No log	52.0	52	0.0000	1.0	1.0	1.0	1.0
No log	53.0	53	0.0000	1.0	1.0	1.0	1.0
No log	54.0	54	0.0000	1.0	1.0	1.0	1.0
No log	55.0	55	0.0000	1.0	1.0	1.0	1.0
No log	56.0	56	0.0000	1.0	1.0	1.0	1.0
No log	57.0	57	0.0000	1.0	1.0	1.0	1.0
No log	58.0	58	0.0000	1.0	1.0	1.0	1.0
No log	59.0	59	0.0000	1.0	1.0	1.0	1.0
No log	60.0	60	0.0000	1.0	1.0	1.0	1.0
No log	61.0	61	0.0000	1.0	1.0	1.0	1.0
No log	62.0	62	0.0000	1.0	1.0	1.0	1.0
No log	63.0	63	0.0000	1.0	1.0	1.0	1.0
No log	64.0	64	0.0000	1.0	1.0	1.0	1.0
No log	65.0	65	0.0000	1.0	1.0	1.0	1.0
No log	66.0	66	0.0000	1.0	1.0	1.0	1.0
No log	67.0	67	0.0000	1.0	1.0	1.0	1.0
No log	68.0	68	0.0000	1.0	1.0	1.0	1.0
No log	69.0	69	0.0000	1.0	1.0	1.0	1.0
No log	70.0	70	0.0000	1.0	1.0	1.0	1.0
No log	71.0	71	0.0000	1.0	1.0	1.0	1.0
No log	72.0	72	0.0000	1.0	1.0	1.0	1.0
No log	73.0	73	0.0000	1.0	1.0	1.0	1.0
No log	74.0	74	0.0000	1.0	1.0	1.0	1.0
No log	75.0	75	0.0000	1.0	1.0	1.0	1.0
No log	76.0	76	0.0000	1.0	1.0	1.0	1.0
No log	77.0	77	0.0000	1.0	1.0	1.0	1.0
No log	78.0	78	0.0000	1.0	1.0	1.0	1.0
No log	79.0	79	0.0000	1.0	1.0	1.0	1.0
No log	80.0	80	0.0000	1.0	1.0	1.0	1.0
No log	81.0	81	0.0000	1.0	1.0	1.0	1.0
No log	82.0	82	0.0000	1.0	1.0	1.0	1.0
No log	83.0	83	0.0000	1.0	1.0	1.0	1.0
No log	84.0	84	0.0000	1.0	1.0	1.0	1.0
No log	85.0	85	0.0000	1.0	1.0	1.0	1.0
No log	86.0	86	0.0000	1.0	1.0	1.0	1.0
No log	87.0	87	0.0000	1.0	1.0	1.0	1.0
No log	88.0	88	0.0000	1.0	1.0	1.0	1.0
No log	89.0	89	0.0000	1.0	1.0	1.0	1.0
No log	90.0	90	0.0000	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

devagonal/mt5-semantic

mt5-semantic

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

devagonal/
mt5-semantic