distilgpt2-finetuned-eap

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.0002, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
4.6280	0.0015	4.3578	0.0013	0
4.2572	0.0018	4.2845	0.0013	1
4.0662	0.0018	4.2587	0.0012	2
3.9066	0.0016	4.2470	0.0012	3
3.7578	0.0016	4.2493	0.0013	4
3.6177	0.0018	4.2728	0.0013	5
3.4741	0.0017	4.3208	0.0012	6
3.3366	0.0017	4.3542	0.0012	7
3.1946	0.0016	4.3973	0.0012	8
3.0581	0.0016	4.4947	0.0014	9
2.9171	0.0017	4.5970	0.0013	10
2.7766	0.0016	4.6691	0.0014	11
2.6373	0.0015	4.7961	0.0012	12
2.4986	0.0014	4.8906	0.0002	13
2.3600	0.0015	4.9836	0.0002	14
2.2307	0.0014	5.1439	0.0001	15
2.1054	0.0015	5.3017	0.0001	16
1.9798	0.0015	5.4037	0.0001	17
1.8679	0.0014	5.5184	0.0001	18
1.7544	0.0013	5.6429	0.0001	19
1.6486	0.0013	5.7368	0.0001	20
1.5492	0.0013	5.8070	0.0001	21
1.4525	0.0013	5.9248	0.0001	22
1.3725	0.0013	5.9879	0.0	23
1.2901	0.0011	6.1063	0.0001	24
1.2178	0.0014	6.1828	0.0001	25
1.1429	0.0012	6.2581	0.0001	26
1.0831	0.0011	6.3003	0.0000	27
1.0266	0.0012	6.3558	0.0001	28
0.9673	0.0012	6.4831	0.0000	29
0.9116	0.0012	6.5555	0.0	30
0.8652	0.0012	6.6239	0.0	31
0.8198	0.0013	6.6751	0.0	32
0.7795	0.0013	6.7499	0.0	33
0.7410	0.0010	6.7741	0.0000	34
0.7015	0.0012	6.8395	0.0000	35
0.6679	0.0011	6.9150	0.0	36
0.6367	0.0010	6.9847	0.0	37
0.6034	0.0011	7.0334	0.0001	38
0.5756	0.0010	7.0516	0.0000	39
0.5442	0.0011	7.1220	0.0	40
0.5188	0.0010	7.1494	0.0000	41
0.4971	0.0010	7.2100	0.0	42
0.4711	0.0010	7.2883	0.0001	43
0.4501	0.0011	7.2946	0.0	44
0.4274	0.0011	7.3313	0.0001	45
0.4066	0.0011	7.3620	0.0000	46
0.3898	0.0011	7.4119	0.0000	47
0.3679	0.0010	7.4769	0.0001	48
0.3557	0.0010	7.4820	0.0	49