Training Loss	Epoch	Step	Validation Loss
6.3722	0.01	1000	6.1947
5.8723	0.03	2000	5.7025
5.5296	0.04	3000	5.4065
5.3667	0.05	4000	5.2681
5.2477	0.07	5000	5.1903
5.1877	0.08	6000	5.0935
5.1422	0.1	7000	5.0452
5.0818	0.11	8000	4.9876
5.104	0.12	9000	4.9677
5.0345	0.14	10000	4.9417
5.0253	0.15	11000	4.8983
4.9818	0.16	12000	4.8814
4.9996	0.18	13000	4.8422
4.9835	0.19	14000	4.8159
4.9212	0.21	15000	4.7951
4.9698	0.22	16000	4.7970
4.9405	0.23	17000	4.7749
4.9429	0.25	18000	4.7551
4.915	0.26	19000	4.7575
4.9308	0.27	20000	4.7387
4.8789	0.29	21000	4.7353
4.9302	0.3	22000	4.7149
4.8546	0.32	23000	4.7028
4.8979	0.33	24000	4.6912
4.8402	0.34	25000	4.6899
4.8194	0.36	26000	4.6762
4.8176	0.37	27000	4.6629
4.881	0.38	28000	4.6611
4.7831	0.4	29000	4.6705
4.8144	0.41	30000	4.6440
4.7651	0.43	31000	4.6262
4.8063	0.44	32000	4.6144
4.8004	0.45	33000	4.5894
4.7853	0.47	34000	4.6104
4.7983	0.48	35000	4.5856
4.7683	0.49	36000	4.5874
4.7828	0.51	37000	4.5970
4.7491	0.52	38000	4.5622
4.6805	0.54	39000	4.5814
4.7669	0.55	40000	4.5596
4.7515	0.56	41000	4.5585
4.7032	0.58	42000	4.5397
4.7043	0.59	43000	4.5224
4.6694	0.6	44000	4.5010
4.6543	0.62	45000	4.5089
4.6597	0.63	46000	4.4671
4.6814	0.65	47000	4.4496
4.6546	0.66	48000	4.4511
4.6576	0.67	49000	4.4552
4.6602	0.69	50000	4.4423
4.656	0.7	51000	4.4215
4.609	0.71	52000	4.4343
4.6102	0.73	53000	4.4076
4.6264	0.74	54000	4.3941
4.6187	0.76	55000	4.3950
4.5811	0.77	56000	4.3798
4.5749	0.78	57000	4.3555
4.4991	0.8	58000	4.3596
4.596	0.81	59000	4.3433
4.5383	0.82	60000	4.3221
4.565	0.84	61000	4.3264
4.4996	0.85	62000	4.3128
4.5016	0.87	63000	4.2906
4.5139	0.88	64000	4.2749
4.4796	0.89	65000	4.2676
4.497	0.91	66000	4.2679
4.459	0.92	67000	4.2367
4.5178	0.93	68000	4.2294
4.3981	0.95	69000	4.2066
4.4179	0.96	70000	4.1949
4.4269	0.98	71000	4.1782
4.3541	0.99	72000	4.1718
4.4032	1.0	73000	4.1599
4.3656	1.02	74000	4.1415
4.3256	1.03	75000	4.1283
4.3469	1.04	76000	4.1057
4.2923	1.06	77000	4.1011
4.3402	1.07	78000	4.0753
4.3233	1.09	79000	4.0664
4.2872	1.1	80000	4.0397
4.2772	1.11	81000	4.0319
4.2364	1.13	82000	4.0200
4.3073	1.14	83000	4.0171
4.1852	1.15	84000	4.0025
4.2935	1.17	85000	3.9770
4.3258	1.18	86000	3.9670
4.2046	1.19	87000	3.9425
4.2298	1.21	88000	3.9377
4.2161	1.22	89000	3.9316
4.1894	1.24	90000	3.9136
4.1873	1.25	91000	3.8801
4.2103	1.26	92000	3.8825
4.1437	1.28	93000	3.8588
4.1136	1.29	94000	3.8515
4.1303	1.3	95000	3.8311
4.2183	1.32	96000	3.8139
4.1434	1.33	97000	3.7916
4.1247	1.35	98000	3.7898
4.0946	1.36	99000	3.7764
4.0452	1.37	100000	3.7577
4.0954	1.39	101000	3.7483
4.1195	1.4	102000	3.7259
4.0884	1.41	103000	3.7151
4.0441	1.43	104000	3.7042
4.0575	1.44	105000	3.6830
4.0668	1.46	106000	3.6778
4.0006	1.47	107000	3.6596
4.0275	1.48	108000	3.6575
4.0107	1.5	109000	3.6429
4.0156	1.51	110000	3.6217
3.9957	1.52	111000	3.6097
3.988	1.54	112000	3.5979
3.9985	1.55	113000	3.5856
3.9697	1.57	114000	3.5750
3.922	1.58	115000	3.5601
3.9144	1.59	116000	3.5500
3.9287	1.61	117000	3.5421
3.9738	1.62	118000	3.5266
3.9523	1.63	119000	3.5256
3.9573	1.65	120000	3.5076
3.9187	1.66	121000	3.4987
3.8782	1.68	122000	3.4914
3.8946	1.69	123000	3.4825
3.9306	1.7	124000	3.4740
3.852	1.72	125000	3.4690
3.8554	1.73	126000	3.4605
3.8673	1.74	127000	3.4530
3.8667	1.76	128000	3.4457
3.8303	1.77	129000	3.4427
3.8967	1.79	130000	3.4384
3.8384	1.8	131000	3.4299
3.8537	1.81	132000	3.4254
3.8981	1.83	133000	3.4217
3.8538	1.84	134000	3.4184
3.9361	1.85	135000	3.4149
3.9096	1.87	136000	3.4111
3.8507	1.88	137000	3.4106
3.8434	1.9	138000	3.4089
3.8476	1.91	139000	3.4070
3.8788	1.92	140000	3.4059
3.8648	1.94	141000	3.4059
3.8596	1.95	142000	3.4051
3.8782	1.96	143000	3.4047
3.7519	1.98	144000	3.4046
3.8754	1.99	145000	3.4046

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

jtatman/headlines

headlines

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jtatman/
headlines