dit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone

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

• Loss: 0.1481
• Accuracy: 0.08
• Brier Loss: 0.9369
• Nll: 9.2883
• F1 Micro: 0.08
• F1 Macro: 0.0357
• Ece: 0.1153
• Aurc: 0.8531

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
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	0.96	12	0.1528	0.0625	0.9377	9.9656	0.0625	0.0074	0.1025	0.9319
No log	1.96	24	0.1507	0.06	0.9377	9.9434	0.06	0.0074	0.1036	0.9537
No log	2.96	36	0.1500	0.0625	0.9376	8.6216	0.0625	0.0074	0.1019	0.9383
No log	3.96	48	0.1498	0.0625	0.9376	9.2776	0.0625	0.0074	0.1032	0.9438
No log	4.96	60	0.1496	0.0625	0.9375	9.3105	0.0625	0.0074	0.1017	0.9421
No log	5.96	72	0.1495	0.0625	0.9375	9.7276	0.0625	0.0074	0.1029	0.9380
No log	6.96	84	0.1494	0.0625	0.9374	9.6348	0.0625	0.0074	0.1017	0.9347
No log	7.96	96	0.1493	0.0625	0.9374	9.6145	0.0625	0.0074	0.1008	0.9359
No log	8.96	108	0.1492	0.0625	0.9374	9.5748	0.0625	0.0074	0.1019	0.9371
No log	9.96	120	0.1491	0.0625	0.9373	9.5551	0.0625	0.0074	0.1005	0.9372
No log	10.96	132	0.1490	0.065	0.9373	9.5267	0.065	0.0122	0.1047	0.9315
No log	11.96	144	0.1489	0.065	0.9373	9.5165	0.065	0.0122	0.1043	0.9284
No log	12.96	156	0.1488	0.065	0.9372	9.5162	0.065	0.0123	0.1068	0.9302
No log	13.96	168	0.1488	0.07	0.9372	9.5139	0.07	0.0213	0.1070	0.9275
No log	14.96	180	0.1487	0.0725	0.9371	9.4579	0.0725	0.0253	0.1095	0.9174
No log	15.96	192	0.1486	0.075	0.9371	9.3950	0.075	0.0286	0.1106	0.9161
No log	16.96	204	0.1485	0.075	0.9371	9.3347	0.075	0.0280	0.1055	0.9014
No log	17.96	216	0.1484	0.0775	0.9370	9.3157	0.0775	0.0315	0.1089	0.8695
No log	18.96	228	0.1483	0.08	0.9370	9.3125	0.08	0.0362	0.1133	0.8526
No log	19.96	240	0.1483	0.08	0.9370	9.2915	0.08	0.0360	0.1113	0.8554
No log	20.96	252	0.1482	0.0775	0.9370	9.2937	0.0775	0.0374	0.1118	0.8475
No log	21.96	264	0.1482	0.08	0.9369	9.2903	0.08	0.0357	0.1167	0.8526
No log	22.96	276	0.1482	0.08	0.9369	9.2888	0.08	0.0357	0.1099	0.8540
No log	23.96	288	0.1481	0.08	0.9369	9.2877	0.08	0.0357	0.1126	0.8531
No log	24.96	300	0.1481	0.08	0.9369	9.2883	0.08	0.0357	0.1153	0.8531

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1.post200
• Datasets 2.9.0
• Tokenizers 0.13.2