This model is a fine-tuned version of openai-community/gpt2 on the gokuls/wiki_book_corpus_raw_dataset_tiny dataset. It achieves the following results on the evaluation set:

Loss: 9.4766
Accuracy: 0.0851

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 36
eval_batch_size: 36
seed: 10
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
10.8672	0.0001	1	10.8672	0.0045
10.8672	0.0001	2	10.8672	0.0045
10.8672	0.0002	3	10.8672	0.0045
10.8672	0.0002	4	10.8672	0.0045
10.8594	0.0003	5	10.8672	0.0045
10.8672	0.0003	6	10.8672	0.0045
10.8672	0.0004	7	10.8672	0.0045
10.8672	0.0004	8	10.8672	0.0045
10.8672	0.0005	9	10.8672	0.0045
10.8594	0.0005	10	10.8672	0.0045
10.8672	0.0006	11	10.8672	0.0045
10.8672	0.0007	12	10.8672	0.0045
10.8594	0.0007	13	10.8672	0.0045
10.8672	0.0008	14	10.8672	0.0045
10.8594	0.0008	15	10.8672	0.0045
10.8594	0.0009	16	10.8672	0.0045
10.8672	0.0009	17	10.8672	0.0045
10.8672	0.0010	18	10.8359	0.0086
10.8359	0.0010	19	10.8047	0.0108
10.8047	0.0011	20	10.7734	0.0113
10.7891	0.0011	21	10.75	0.0115
10.7578	0.0012	22	10.7266	0.0119
10.7188	0.0013	23	10.7031	0.0129
10.7188	0.0013	24	10.6797	0.0147
10.6953	0.0014	25	10.6641	0.0179
10.6719	0.0014	26	10.6406	0.0231
10.6562	0.0015	27	10.625	0.0286
10.6641	0.0015	28	10.6094	0.0347
10.6328	0.0016	29	10.5938	0.0399
10.6016	0.0016	30	10.5781	0.0436
10.6016	0.0017	31	10.5703	0.0463
10.5938	0.0017	32	10.5547	0.0479
10.5781	0.0018	33	10.5469	0.0484
10.5547	0.0019	34	10.5312	0.0484
10.5469	0.0019	35	10.5234	0.0484
10.5391	0.0020	36	10.5156	0.0482
10.5312	0.0020	37	10.5078	0.0475
10.5312	0.0021	38	10.4922	0.0475
10.4922	0.0021	39	10.4844	0.0476
10.5078	0.0022	40	10.4844	0.0477
10.4922	0.0022	41	10.4766	0.0481
10.4844	0.0023	42	10.4688	0.0486
10.4766	0.0023	43	10.4609	0.0493
10.4844	0.0024	44	10.4531	0.0495
10.4688	0.0025	45	10.4453	0.0503
10.4844	0.0025	46	10.4453	0.0513
10.4609	0.0026	47	10.4375	0.0522
10.4453	0.0026	48	10.4297	0.0526
10.4453	0.0027	49	10.4297	0.0532
10.4297	0.0027	50	10.4219	0.0537
10.4219	0.0028	51	10.4141	0.0544
10.4297	0.0028	52	10.4141	0.0548
10.4375	0.0029	53	10.4062	0.0554
10.4219	0.0029	54	10.4062	0.0558
10.4141	0.0030	55	10.3984	0.0565
10.4141	0.0031	56	10.3906	0.0574
10.4219	0.0031	57	10.3906	0.0583
10.4219	0.0032	58	10.3828	0.0591
10.3984	0.0032	59	10.3828	0.0598
10.3984	0.0033	60	10.375	0.0603
10.3984	0.0033	61	10.375	0.0607
10.3906	0.0034	62	10.3672	0.0611
10.3672	0.0034	63	10.3672	0.0615
10.3906	0.0035	64	10.3594	0.0616
10.3828	0.0035	65	10.3594	0.0615
10.3594	0.0036	66	10.3516	0.0614
10.3516	0.0037	67	10.3438	0.0610
10.3516	0.0037	68	10.3438	0.0609
10.3438	0.0038	69	10.3359	0.0611
10.3594	0.0038	70	10.3359	0.0610
10.3594	0.0039	71	10.3281	0.0610
10.3203	0.0039	72	10.3281	0.0610
10.3516	0.0040	73	10.3203	0.0610
10.3203	0.0040	74	10.3125	0.0611
10.3281	0.0041	75	10.3125	0.0612
10.3438	0.0041	76	10.3047	0.0614
10.2969	0.0042	77	10.3047	0.0618
10.3281	0.0043	78	10.2969	0.0622
10.2891	0.0043	79	10.2969	0.0628
10.3047	0.0044	80	10.2891	0.0632
10.2969	0.0044	81	10.2812	0.0637
10.2891	0.0045	82	10.2812	0.0643
10.3125	0.0045	83	10.2734	0.0649
10.2891	0.0046	84	10.2734	0.0654
10.2812	0.0046	85	10.2656	0.0657
10.3047	0.0047	86	10.2656	0.0659
10.2969	0.0047	87	10.2578	0.0660
10.2578	0.0048	88	10.25	0.0661
10.2812	0.0048	89	10.25	0.0662
10.2734	0.0049	90	10.2422	0.0663
10.2891	0.0050	91	10.2422	0.0664
10.2578	0.0050	92	10.2344	0.0666
10.2734	0.0051	93	10.2344	0.0668
10.2266	0.0051	94	10.2266	0.0671
10.2578	0.0052	95	10.2266	0.0674
10.25	0.0052	96	10.2188	0.0676
10.2266	0.0053	97	10.2188	0.0678
10.2266	0.0053	98	10.2109	0.0679
10.2344	0.0054	99	10.2109	0.0681
10.2422	0.0054	100	10.2031	0.0682
10.2422	0.0055	101	10.2031	0.0683
10.2266	0.0056	102	10.1953	0.0685
10.2188	0.0056	103	10.1953	0.0686
10.2109	0.0057	104	10.1875	0.0687
10.1797	0.0057	105	10.1875	0.0689
10.1797	0.0058	106	10.1797	0.0691
10.1719	0.0058	107	10.1797	0.0693
10.1875	0.0059	108	10.1719	0.0696
10.1797	0.0059	109	10.1719	0.0698
10.1797	0.0060	110	10.1641	0.0700
10.1406	0.0060	111	10.1641	0.0702
10.1719	0.0061	112	10.1641	0.0704
10.1953	0.0062	113	10.1562	0.0706
10.1719	0.0062	114	10.1562	0.0708
10.1641	0.0063	115	10.1484	0.0710
10.1719	0.0063	116	10.1484	0.0712
10.1484	0.0064	117	10.1406	0.0713
10.1562	0.0064	118	10.1406	0.0715
10.1562	0.0065	119	10.1328	0.0716
10.1484	0.0065	120	10.1328	0.0718
10.1406	0.0066	121	10.125	0.0719
10.1328	0.0066	122	10.125	0.0721
10.1641	0.0067	123	10.1172	0.0722
10.1328	0.0068	124	10.1172	0.0723
10.1484	0.0068	125	10.1094	0.0725
10.1406	0.0069	126	10.1094	0.0726
10.1406	0.0069	127	10.1016	0.0728
10.125	0.0070	128	10.1016	0.0729
10.1172	0.0070	129	10.0938	0.0731
10.1016	0.0071	130	10.0938	0.0732
10.1172	0.0071	131	10.0859	0.0733
10.1172	0.0072	132	10.0859	0.0734
10.1172	0.0072	133	10.0859	0.0736
10.0938	0.0073	134	10.0781	0.0737
10.1094	0.0074	135	10.0781	0.0738
10.1094	0.0074	136	10.0703	0.0740
10.0703	0.0075	137	10.0703	0.0742
10.0781	0.0075	138	10.0625	0.0743
10.0781	0.0076	139	10.0625	0.0745
10.0781	0.0076	140	10.0547	0.0746
10.0625	0.0077	141	10.0547	0.0747
10.0781	0.0077	142	10.0469	0.0749
10.0391	0.0078	143	10.0469	0.0750
10.0703	0.0078	144	10.0469	0.0751
10.0391	0.0079	145	10.0391	0.0753
10.0469	0.0080	146	10.0391	0.0754
10.0547	0.0080	147	10.0312	0.0755
10.0703	0.0081	148	10.0312	0.0756
10.0469	0.0081	149	10.0234	0.0757
10.0391	0.0082	150	10.0234	0.0759
10.0391	0.0082	151	10.0156	0.0760
10.0391	0.0083	152	10.0156	0.0761
10.0391	0.0083	153	10.0156	0.0762
10.0469	0.0084	154	10.0078	0.0763
10.0312	0.0084	155	10.0078	0.0765
9.9844	0.0085	156	10.0	0.0766
10.0	0.0086	157	10.0	0.0767
10.0078	0.0086	158	9.9922	0.0768
10.0078	0.0087	159	9.9922	0.0769
10.0234	0.0087	160	9.9922	0.0770
9.9922	0.0088	161	9.9844	0.0771
9.9922	0.0088	162	9.9844	0.0772
9.9766	0.0089	163	9.9766	0.0773
9.9922	0.0089	164	9.9766	0.0773
9.9766	0.0090	165	9.9688	0.0774
9.9844	0.0090	166	9.9688	0.0775
9.9766	0.0091	167	9.9688	0.0776
9.9844	0.0092	168	9.9609	0.0777
9.9609	0.0092	169	9.9609	0.0778
9.9766	0.0093	170	9.9531	0.0778
9.9531	0.0093	171	9.9531	0.0779
9.9922	0.0094	172	9.9531	0.0780
9.9531	0.0094	173	9.9453	0.0781
9.9375	0.0095	174	9.9453	0.0781
9.9688	0.0095	175	9.9375	0.0782
9.9453	0.0096	176	9.9375	0.0783
9.9375	0.0096	177	9.9375	0.0783
9.9375	0.0097	178	9.9297	0.0784
9.9453	0.0098	179	9.9297	0.0785
9.9453	0.0098	180	9.9219	0.0786
9.9297	0.0099	181	9.9219	0.0787
9.9375	0.0099	182	9.9141	0.0787
9.9375	0.0100	183	9.9141	0.0788
9.8984	0.0100	184	9.9141	0.0789
9.9375	0.0101	185	9.9062	0.0790
9.9297	0.0101	186	9.9062	0.0791
9.9297	0.0102	187	9.8984	0.0791
9.9141	0.0102	188	9.8984	0.0792
9.9219	0.0103	189	9.8984	0.0793
9.8984	0.0104	190	9.8906	0.0793
9.8828	0.0104	191	9.8906	0.0794
9.8984	0.0105	192	9.8828	0.0795
9.8906	0.0105	193	9.8828	0.0796
9.9062	0.0106	194	9.8828	0.0797
9.875	0.0106	195	9.875	0.0798
9.8594	0.0107	196	9.875	0.0798
9.8828	0.0107	197	9.875	0.0799
9.8984	0.0108	198	9.8672	0.0800
9.8906	0.0108	199	9.8672	0.0801
9.9062	0.0109	200	9.8594	0.0801
9.8672	0.0110	201	9.8594	0.0802
9.8672	0.0110	202	9.8594	0.0803
9.8906	0.0111	203	9.8516	0.0804
9.8828	0.0111	204	9.8516	0.0804
9.8906	0.0112	205	9.8438	0.0805
9.8828	0.0112	206	9.8438	0.0805
9.8594	0.0113	207	9.8438	0.0806
9.875	0.0113	208	9.8359	0.0806
9.8594	0.0114	209	9.8359	0.0807
9.8516	0.0114	210	9.8281	0.0808
9.8359	0.0115	211	9.8281	0.0809
9.8281	0.0116	212	9.8281	0.0810
9.8516	0.0116	213	9.8203	0.0810
9.8516	0.0117	214	9.8203	0.0811
9.8281	0.0117	215	9.8203	0.0811
9.8438	0.0118	216	9.8125	0.0812
9.8359	0.0118	217	9.8125	0.0813
9.8281	0.0119	218	9.8047	0.0814
9.8281	0.0119	219	9.8047	0.0815
9.8281	0.0120	220	9.8047	0.0815
9.7969	0.0120	221	9.7969	0.0816
9.8281	0.0121	222	9.7969	0.0816
9.8047	0.0122	223	9.7891	0.0817
9.8047	0.0122	224	9.7891	0.0818
9.8047	0.0123	225	9.7891	0.0818
9.8047	0.0123	226	9.7812	0.0819
9.8281	0.0124	227	9.7812	0.0819
9.7812	0.0124	228	9.7812	0.0819
9.7891	0.0125	229	9.7734	0.0820
9.7969	0.0125	230	9.7734	0.0821
9.7578	0.0126	231	9.7656	0.0821
9.8125	0.0126	232	9.7656	0.0822
9.7734	0.0127	233	9.7656	0.0823
9.7656	0.0128	234	9.7578	0.0823
9.7578	0.0128	235	9.7578	0.0824
9.7891	0.0129	236	9.7578	0.0824
9.7812	0.0129	237	9.75	0.0824
9.7656	0.0130	238	9.75	0.0825
9.7969	0.0130	239	9.75	0.0825
9.75	0.0131	240	9.7422	0.0825
9.7734	0.0131	241	9.7422	0.0825
9.7578	0.0132	242	9.7344	0.0825
9.7656	0.0132	243	9.7344	0.0825
9.7266	0.0133	244	9.7344	0.0826
9.75	0.0134	245	9.7266	0.0826
9.7422	0.0134	246	9.7266	0.0827
9.75	0.0135	247	9.7266	0.0827
9.7656	0.0135	248	9.7188	0.0828
9.7266	0.0136	249	9.7188	0.0828
9.75	0.0136	250	9.7109	0.0828
9.7266	0.0137	251	9.7109	0.0829
9.7266	0.0137	252	9.7109	0.0829
9.7266	0.0138	253	9.7031	0.0829
9.7266	0.0138	254	9.7031	0.0829
9.7344	0.0139	255	9.7031	0.0829
9.7109	0.0139	256	9.6953	0.0829
9.7109	0.0140	257	9.6953	0.0829
9.7109	0.0141	258	9.6953	0.0830
9.7031	0.0141	259	9.6875	0.0830
9.7109	0.0142	260	9.6875	0.0831
9.6953	0.0142	261	9.6797	0.0832
9.7031	0.0143	262	9.6797	0.0832
9.6953	0.0143	263	9.6797	0.0832
9.6875	0.0144	264	9.6719	0.0833
9.6719	0.0144	265	9.6719	0.0833
9.6797	0.0145	266	9.6719	0.0832
9.7188	0.0145	267	9.6641	0.0833
9.6953	0.0146	268	9.6641	0.0833
9.6797	0.0147	269	9.6641	0.0833
9.6719	0.0147	270	9.6562	0.0834
9.6875	0.0148	271	9.6562	0.0834
9.6641	0.0148	272	9.6484	0.0835
9.6719	0.0149	273	9.6484	0.0836
9.6719	0.0149	274	9.6484	0.0836
9.6406	0.0150	275	9.6406	0.0837
9.6641	0.0150	276	9.6406	0.0837
9.6328	0.0151	277	9.6406	0.0838
9.6328	0.0151	278	9.6328	0.0838
9.6484	0.0152	279	9.6328	0.0838
9.6484	0.0153	280	9.6328	0.0838
9.6875	0.0153	281	9.625	0.0838
9.6328	0.0154	282	9.625	0.0838
9.6562	0.0154	283	9.6172	0.0838
9.6719	0.0155	284	9.6172	0.0838
9.6641	0.0155	285	9.6172	0.0838
9.6328	0.0156	286	9.6094	0.0838
9.6328	0.0156	287	9.6094	0.0839
9.625	0.0157	288	9.6094	0.0839
9.6328	0.0157	289	9.6016	0.0840
9.6172	0.0158	290	9.6016	0.0840
9.6172	0.0159	291	9.6016	0.0841
9.6094	0.0159	292	9.5938	0.0841
9.6172	0.0160	293	9.5938	0.0842
9.6094	0.0160	294	9.5938	0.0842
9.6328	0.0161	295	9.5859	0.0842
9.5938	0.0161	296	9.5859	0.0842
9.5938	0.0162	297	9.5781	0.0842
9.6016	0.0162	298	9.5781	0.0842
9.5781	0.0163	299	9.5781	0.0842
9.5938	0.0163	300	9.5703	0.0843
9.5938	0.0164	301	9.5703	0.0843
9.6016	0.0165	302	9.5703	0.0844
9.5781	0.0165	303	9.5625	0.0845
9.6016	0.0166	304	9.5625	0.0845
9.5703	0.0166	305	9.5625	0.0845
9.5781	0.0167	306	9.5547	0.0845
9.5938	0.0167	307	9.5547	0.0846
9.5391	0.0168	308	9.5547	0.0846
9.5625	0.0168	309	9.5469	0.0846
9.5547	0.0169	310	9.5469	0.0846
9.5703	0.0169	311	9.5469	0.0846
9.5625	0.0170	312	9.5391	0.0846
9.5469	0.0171	313	9.5391	0.0846
9.5469	0.0171	314	9.5391	0.0846
9.5391	0.0172	315	9.5312	0.0847
9.5781	0.0172	316	9.5312	0.0847
9.5469	0.0173	317	9.5312	0.0847
9.5312	0.0173	318	9.5234	0.0848
9.5703	0.0174	319	9.5234	0.0848
9.5312	0.0174	320	9.5234	0.0848
9.5703	0.0175	321	9.5156	0.0848
9.5312	0.0175	322	9.5156	0.0849
9.5391	0.0176	323	9.5078	0.0849
9.5156	0.0177	324	9.5078	0.0849
9.5234	0.0177	325	9.5078	0.0849
9.5391	0.0178	326	9.5	0.0849
9.5078	0.0178	327	9.5	0.0849
9.5312	0.0179	328	9.5	0.0848
9.5078	0.0179	329	9.4922	0.0848
9.5234	0.0180	330	9.4922	0.0847
9.5078	0.0180	331	9.4922	0.0848
9.4922	0.0181	332	9.4844	0.0848
9.5	0.0181	333	9.4844	0.0849
9.5078	0.0182	334	9.4844	0.0850
9.4766	0.0183	335	9.4766	0.0851
9.5	0.0183	336	9.4766	0.0851

Framework versions

Transformers 4.41.2
Pytorch 2.1.0a0+32f93b1
Datasets 2.20.0
Tokenizers 0.19.1

gokulsrinivasagan/gpt_train_6_256

gpt_train_6_256

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokulsrinivasagan/
gpt_train_6_256