results

This model is a fine-tuned version of prajjwal1/bert-tiny on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0081
• Accuracy: 1.0
• F1: 1.0
• Precision: 1.0
• Recall: 1.0

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	125	1.4595	0.666	0.5919	0.7955	0.6412
No log	2.0	250	1.2117	0.894	0.8814	0.8937	0.8839
No log	3.0	375	0.9703	0.924	0.9164	0.9352	0.9149
1.2705	4.0	500	0.7647	0.934	0.9284	0.9428	0.9262
1.2705	5.0	625	0.5898	0.97	0.9664	0.9722	0.9659
1.2705	6.0	750	0.4600	0.97	0.9664	0.9722	0.9659
1.2705	7.0	875	0.3596	0.97	0.9664	0.9722	0.9659
0.5486	8.0	1000	0.2753	0.97	0.9664	0.9722	0.9659
0.5486	9.0	1125	0.1988	1.0	1.0	1.0	1.0
0.5486	10.0	1250	0.1469	1.0	1.0	1.0	1.0
0.5486	11.0	1375	0.1139	1.0	1.0	1.0	1.0
0.1935	12.0	1500	0.0904	1.0	1.0	1.0	1.0
0.1935	13.0	1625	0.0743	1.0	1.0	1.0	1.0
0.1935	14.0	1750	0.0630	1.0	1.0	1.0	1.0
0.1935	15.0	1875	0.0542	1.0	1.0	1.0	1.0
0.0781	16.0	2000	0.0473	1.0	1.0	1.0	1.0
0.0781	17.0	2125	0.0418	1.0	1.0	1.0	1.0
0.0781	18.0	2250	0.0374	1.0	1.0	1.0	1.0
0.0781	19.0	2375	0.0337	1.0	1.0	1.0	1.0
0.0449	20.0	2500	0.0305	1.0	1.0	1.0	1.0
0.0449	21.0	2625	0.0279	1.0	1.0	1.0	1.0
0.0449	22.0	2750	0.0256	1.0	1.0	1.0	1.0
0.0449	23.0	2875	0.0236	1.0	1.0	1.0	1.0
0.0305	24.0	3000	0.0219	1.0	1.0	1.0	1.0
0.0305	25.0	3125	0.0204	1.0	1.0	1.0	1.0
0.0305	26.0	3250	0.0190	1.0	1.0	1.0	1.0
0.0305	27.0	3375	0.0178	1.0	1.0	1.0	1.0
0.0224	28.0	3500	0.0167	1.0	1.0	1.0	1.0
0.0224	29.0	3625	0.0157	1.0	1.0	1.0	1.0
0.0224	30.0	3750	0.0149	1.0	1.0	1.0	1.0
0.0224	31.0	3875	0.0141	1.0	1.0	1.0	1.0
0.0181	32.0	4000	0.0134	1.0	1.0	1.0	1.0
0.0181	33.0	4125	0.0127	1.0	1.0	1.0	1.0
0.0181	34.0	4250	0.0121	1.0	1.0	1.0	1.0
0.0181	35.0	4375	0.0116	1.0	1.0	1.0	1.0
0.0141	36.0	4500	0.0111	1.0	1.0	1.0	1.0
0.0141	37.0	4625	0.0107	1.0	1.0	1.0	1.0
0.0141	38.0	4750	0.0103	1.0	1.0	1.0	1.0
0.0141	39.0	4875	0.0099	1.0	1.0	1.0	1.0
0.0120	40.0	5000	0.0096	1.0	1.0	1.0	1.0
0.0120	41.0	5125	0.0093	1.0	1.0	1.0	1.0
0.0120	42.0	5250	0.0091	1.0	1.0	1.0	1.0
0.0120	43.0	5375	0.0088	1.0	1.0	1.0	1.0
0.0108	44.0	5500	0.0087	1.0	1.0	1.0	1.0
0.0108	45.0	5625	0.0085	1.0	1.0	1.0	1.0
0.0108	46.0	5750	0.0083	1.0	1.0	1.0	1.0
0.0108	47.0	5875	0.0082	1.0	1.0	1.0	1.0
0.0096	48.0	6000	0.0082	1.0	1.0	1.0	1.0
0.0096	49.0	6125	0.0081	1.0	1.0	1.0	1.0
0.0096	50.0	6250	0.0081	1.0	1.0	1.0	1.0

Framework versions

• Transformers 5.0.0
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.2