Training Loss	Epoch	Step	Validation Loss
5.8245	0.01	1	5.0320
5.1946	0.01	2	4.2171
4.1809	0.02	3	3.4349
3.3553	0.02	4	2.7171
2.185	0.03	5	2.0634
1.9955	0.03	6	1.5786
1.9371	0.04	7	1.2490
1.4402	0.04	8	1.0349
0.8763	0.05	9	0.9157
0.8813	0.05	10	0.8550
0.7723	0.06	11	0.8259
0.7909	0.06	12	0.8052
0.4889	0.07	13	0.7959
0.7361	0.07	14	0.7891
0.4922	0.08	15	0.7793
0.5533	0.09	16	0.7675
1.1071	0.09	17	0.7563
0.7885	0.1	18	0.7480
0.7701	0.1	19	0.7445
0.6235	0.11	20	0.7447
0.8623	0.11	21	0.7484
0.665	0.12	22	0.7558
0.6907	0.12	23	0.7573
0.7143	0.13	24	0.7583
0.7554	0.13	25	0.7599
0.6228	0.14	26	0.7621
0.8079	0.14	27	0.7612
0.6974	0.15	28	0.7586
0.8349	0.16	29	0.7541
0.8251	0.16	30	0.7484
0.687	0.17	31	0.7400
0.8156	0.17	32	0.7280
0.7693	0.18	33	0.7183
0.5224	0.18	34	0.7096
0.6345	0.19	35	0.7033
0.6443	0.19	36	0.6979
1.1552	0.2	37	0.6930
0.7819	0.2	38	0.6897
0.6277	0.21	39	0.6875
0.5751	0.21	40	0.6862
0.7169	0.22	41	0.6854
0.7077	0.22	42	0.6842
0.5667	0.23	43	0.6831
0.9234	0.24	44	0.6822
0.6332	0.24	45	0.6815
0.865	0.25	46	0.6806
0.5918	0.25	47	0.6797
0.6196	0.26	48	0.6788
0.7697	0.26	49	0.6778
0.4448	0.27	50	0.6769
0.7951	0.27	51	0.6760
0.9171	0.28	52	0.6751
0.7169	0.28	53	0.6745
0.7001	0.29	54	0.6742
0.7755	0.29	55	0.6742
0.7426	0.3	56	0.6743
0.6208	0.3	57	0.6742
0.6962	0.31	58	0.6740
0.3848	0.32	59	0.6739
0.6986	0.32	60	0.6736
0.6316	0.33	61	0.6734
0.5988	0.33	62	0.6732
0.6551	0.34	63	0.6729
0.6102	0.34	64	0.6724
0.7752	0.35	65	0.6718
0.6145	0.35	66	0.6713
0.6829	0.36	67	0.6709
0.7952	0.36	68	0.6705
0.5888	0.37	69	0.6702
0.7763	0.37	70	0.6698
0.6723	0.38	71	0.6694
0.6429	0.39	72	0.6691
1.0005	0.39	73	0.6688
0.6184	0.4	74	0.6684
0.7118	0.4	75	0.6682
0.5414	0.41	76	0.6679
0.6491	0.41	77	0.6676
0.9418	0.42	78	0.6673
0.7183	0.42	79	0.6670
0.682	0.43	80	0.6668
0.5946	0.43	81	0.6665
0.6681	0.44	82	0.6662
0.9125	0.44	83	0.6659
0.6752	0.45	84	0.6657
0.6908	0.45	85	0.6655
0.5878	0.46	86	0.6653
0.805	0.47	87	0.6651
0.7584	0.47	88	0.6650
0.6652	0.48	89	0.6649
0.9363	0.48	90	0.6647
0.6201	0.49	91	0.6646
0.6827	0.49	92	0.6644
0.8921	0.5	93	0.6643
0.5194	0.5	94	0.6641
0.9393	0.51	95	0.6639
0.8484	0.51	96	0.6637
0.5412	0.52	97	0.6635
1.0085	0.52	98	0.6633
0.5217	0.53	99	0.6632
0.6137	0.53	100	0.6630
0.5484	0.54	101	0.6629
0.5827	0.55	102	0.6627
0.3374	0.55	103	0.6629
0.8269	0.56	104	0.6630
0.8126	0.56	105	0.6630
0.8088	0.57	106	0.6631
0.5498	0.57	107	0.6632
0.6787	0.58	108	0.6633
0.8786	0.58	109	0.6633
0.6237	0.59	110	0.6634
0.6369	0.59	111	0.6634
0.5629	0.6	112	0.6634
0.4571	0.6	113	0.6635
0.902	0.61	114	0.6634
0.5153	0.61	115	0.6632
0.9284	0.62	116	0.6629
0.7149	0.63	117	0.6626
0.5224	0.63	118	0.6623
0.5969	0.64	119	0.6621
0.655	0.64	120	0.6619
0.6182	0.65	121	0.6619
0.6564	0.65	122	0.6618
0.6919	0.66	123	0.6618
0.5894	0.66	124	0.6617
0.4312	0.67	125	0.6617
0.7523	0.67	126	0.6617
0.7962	0.68	127	0.6617
0.3758	0.68	128	0.6617
0.7343	0.69	129	0.6617
0.7569	0.7	130	0.6616
0.4816	0.7	131	0.6616
0.7127	0.71	132	0.6616
0.4597	0.71	133	0.6616
0.6429	0.72	134	0.6616
0.6452	0.72	135	0.6616
0.5815	0.73	136	0.6615
0.743	0.73	137	0.6614
0.5613	0.74	138	0.6612
0.5038	0.74	139	0.6610
0.797	0.75	140	0.6609
0.6244	0.75	141	0.6608
0.4257	0.76	142	0.6607
0.6096	0.76	143	0.6606
0.6566	0.77	144	0.6605
0.4325	0.78	145	0.6604
0.7307	0.78	146	0.6604
0.7955	0.79	147	0.6603
0.6972	0.79	148	0.6602
0.7527	0.8	149	0.6602
0.5718	0.8	150	0.6602
0.8002	0.81	151	0.6602
0.6643	0.81	152	0.6602
0.7817	0.82	153	0.6602
0.6829	0.82	154	0.6602
0.8392	0.83	155	0.6601
0.5246	0.83	156	0.6601
0.6613	0.84	157	0.6601
0.4456	0.84	158	0.6600
0.4505	0.85	159	0.6600
0.6184	0.86	160	0.6600
0.6419	0.86	161	0.6599
0.3138	0.87	162	0.6599
0.5554	0.87	163	0.6598
0.702	0.88	164	0.6597
0.801	0.88	165	0.6595
0.6689	0.89	166	0.6594
0.5907	0.89	167	0.6593
0.9349	0.9	168	0.6592
0.7987	0.9	169	0.6591
0.6379	0.91	170	0.6590
0.5561	0.91	171	0.6589
0.6637	0.92	172	0.6589
0.5391	0.93	173	0.6588
0.6578	0.93	174	0.6588
0.7013	0.94	175	0.6587
0.6868	0.94	176	0.6587
0.6297	0.95	177	0.6586
0.7349	0.95	178	0.6586
0.8577	0.96	179	0.6585
0.8536	0.96	180	0.6585
0.4971	0.97	181	0.6585
0.5129	0.97	182	0.6585
0.7636	0.98	183	0.6585
0.5111	0.98	184	0.6585
0.7281	0.99	185	0.6585
0.5653	0.99	186	0.6585
0.7766	1.0	187	0.6584

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3

ogimgio/gpt-neo-125m-neurallinguisticpioneers

gpt-neo-125m-neurallinguisticpioneers

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ogimgio/
gpt-neo-125m-neurallinguisticpioneers