47015772_1

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

• Loss: 0.6501
• Accuracy: 0.0001

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: 1.41e-05
• train_batch_size: 32
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1019	0.03	25	0.9747	0.0001
0.8784	0.06	50	0.8178	0.0001
0.8308	0.09	75	0.7727	0.0002
0.7975	0.11	100	0.7508	0.0001
0.787	0.14	125	0.7370	0.0001
0.7752	0.17	150	0.7279	0.0001
0.7703	0.2	175	0.7210	0.0001
0.7584	0.23	200	0.7157	0.0001
0.7662	0.26	225	0.7108	0.0001
0.7431	0.28	250	0.7073	0.0001
0.7437	0.31	275	0.7038	0.0001
0.7378	0.34	300	0.7006	0.0001
0.7252	0.37	325	0.6974	0.0001
0.7242	0.4	350	0.6948	0.0001
0.7284	0.43	375	0.6922	0.0001
0.7187	0.46	400	0.6898	0.0001
0.7169	0.48	425	0.6878	0.0001
0.723	0.51	450	0.6858	0.0001
0.7189	0.54	475	0.6834	0.0001
0.7142	0.57	500	0.6818	0.0001
0.7071	0.6	525	0.6798	0.0001
0.7131	0.63	550	0.6782	0.0001
0.7107	0.66	575	0.6768	0.0001
0.702	0.68	600	0.6761	0.0001
0.6937	0.71	625	0.6742	0.0001
0.6984	0.74	650	0.6732	0.0001
0.7023	0.77	675	0.6720	0.0001
0.7002	0.8	700	0.6708	0.0001
0.7057	0.83	725	0.6697	0.0001
0.6991	0.85	750	0.6687	0.0001
0.6951	0.88	775	0.6674	0.0001
0.7101	0.91	800	0.6668	0.0001
0.6931	0.94	825	0.6661	0.0001
0.6866	0.97	850	0.6652	0.0001
0.702	1.0	875	0.6643	0.0001
0.6996	1.03	900	0.6637	0.0001
0.7077	1.05	925	0.6629	0.0001
0.6955	1.08	950	0.6623	0.0001
0.6947	1.11	975	0.6619	0.0001
0.6933	1.14	1000	0.6612	0.0001
0.6855	1.17	1025	0.6601	0.0001
0.7004	1.2	1050	0.6597	0.0001
0.6934	1.22	1075	0.6594	0.0001
0.6863	1.25	1100	0.6585	0.0001
0.6886	1.28	1125	0.6580	0.0001
0.6851	1.31	1150	0.6579	0.0001
0.683	1.34	1175	0.6574	0.0001
0.703	1.37	1200	0.6570	0.0001
0.6792	1.4	1225	0.6564	0.0001
0.6849	1.42	1250	0.6558	0.0001
0.6856	1.45	1275	0.6556	0.0001
0.6856	1.48	1300	0.6551	0.0001
0.6856	1.51	1325	0.6550	0.0001
0.6857	1.54	1350	0.6543	0.0001
0.6856	1.57	1375	0.6539	0.0001
0.689	1.59	1400	0.6538	0.0001
0.6892	1.62	1425	0.6534	0.0001
0.6823	1.65	1450	0.6532	0.0001
0.6828	1.68	1475	0.6529	0.0001
0.6864	1.71	1500	0.6528	0.0001
0.6886	1.74	1525	0.6523	0.0001
0.6642	1.77	1550	0.6521	0.0001
0.6849	1.79	1575	0.6519	0.0001
0.6834	1.82	1600	0.6516	0.0001
0.6839	1.85	1625	0.6515	0.0001
0.6856	1.88	1650	0.6514	0.0001
0.6725	1.91	1675	0.6511	0.0001
0.6813	1.94	1700	0.6509	0.0001
0.6832	1.97	1725	0.6508	0.0001
0.6739	1.99	1750	0.6508	0.0001
0.6716	2.02	1775	0.6506	0.0001
0.6798	2.05	1800	0.6505	0.0001
0.6758	2.08	1825	0.6503	0.0001
0.6791	2.11	1850	0.6503	0.0001
0.6735	2.14	1875	0.6503	0.0001
0.6887	2.16	1900	0.6502	0.0001
0.686	2.19	1925	0.6502	0.0001
0.682	2.22	1950	0.6501	0.0001
0.675	2.25	1975	0.6501	0.0001
0.6792	2.28	2000	0.6501	0.0001

Framework versions

• Transformers 4.37.0
• Pytorch 2.0.0+cu118
• Datasets 2.16.1
• Tokenizers 0.15.1