distilbert-base-uncased__subj__train-8-4

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3305
• Accuracy: 0.8565

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6991	1.0	3	0.6772	0.75
0.6707	2.0	6	0.6704	0.75
0.6402	3.0	9	0.6608	1.0
0.5789	4.0	12	0.6547	0.75
0.5211	5.0	15	0.6434	0.75
0.454	6.0	18	0.6102	1.0
0.4187	7.0	21	0.5701	1.0
0.3401	8.0	24	0.5289	1.0
0.3107	9.0	27	0.4737	1.0
0.2381	10.0	30	0.4255	1.0
0.1982	11.0	33	0.3685	1.0
0.1631	12.0	36	0.3200	1.0
0.1234	13.0	39	0.2798	1.0
0.0993	14.0	42	0.2455	1.0
0.0781	15.0	45	0.2135	1.0
0.0586	16.0	48	0.1891	1.0
0.0513	17.0	51	0.1671	1.0
0.043	18.0	54	0.1427	1.0
0.0307	19.0	57	0.1225	1.0
0.0273	20.0	60	0.1060	1.0
0.0266	21.0	63	0.0920	1.0
0.0233	22.0	66	0.0823	1.0
0.0185	23.0	69	0.0751	1.0
0.0173	24.0	72	0.0698	1.0
0.0172	25.0	75	0.0651	1.0
0.0142	26.0	78	0.0613	1.0
0.0151	27.0	81	0.0583	1.0
0.0117	28.0	84	0.0563	1.0
0.0123	29.0	87	0.0546	1.0
0.0121	30.0	90	0.0531	1.0
0.0123	31.0	93	0.0511	1.0
0.0112	32.0	96	0.0496	1.0
0.0103	33.0	99	0.0481	1.0
0.0086	34.0	102	0.0468	1.0
0.0096	35.0	105	0.0457	1.0
0.0107	36.0	108	0.0447	1.0
0.0095	37.0	111	0.0439	1.0
0.0102	38.0	114	0.0429	1.0
0.0077	39.0	117	0.0422	1.0
0.0092	40.0	120	0.0415	1.0
0.0083	41.0	123	0.0409	1.0
0.0094	42.0	126	0.0404	1.0
0.0084	43.0	129	0.0400	1.0
0.0085	44.0	132	0.0396	1.0
0.0092	45.0	135	0.0392	1.0
0.0076	46.0	138	0.0389	1.0
0.0073	47.0	141	0.0388	1.0
0.0085	48.0	144	0.0387	1.0
0.0071	49.0	147	0.0386	1.0
0.0079	50.0	150	0.0386	1.0

Framework versions

• Transformers 4.15.0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2
• Tokenizers 0.10.3