model_ckpt

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: 0.0091
• Accuracy: 0.9989
• F1: 0.9989
• Precision: 0.9989
• Recall: 0.9989

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: 0.0001
• train_batch_size: 116
• eval_batch_size: 116
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.3936	1.0	45	0.0168	1.0	1.0	1.0	1.0
0.0187	2.0	90	0.0047	0.9989	0.9989	0.9989	0.9989
0.0049	3.0	135	0.0345	0.9945	0.9945	0.9945	0.9945
0.0035	4.0	180	0.0105	0.9978	0.9978	0.9978	0.9978
0.0015	5.0	225	0.0075	0.9989	0.9989	0.9989	0.9989
0.0009	6.0	270	0.0077	0.9989	0.9989	0.9989	0.9989
0.0007	7.0	315	0.0078	0.9989	0.9989	0.9989	0.9989
0.0006	8.0	360	0.0079	0.9989	0.9989	0.9989	0.9989
0.0005	9.0	405	0.0081	0.9989	0.9989	0.9989	0.9989
0.0004	10.0	450	0.0083	0.9989	0.9989	0.9989	0.9989
0.0004	11.0	495	0.0085	0.9989	0.9989	0.9989	0.9989
0.0003	12.0	540	0.0086	0.9989	0.9989	0.9989	0.9989
0.0003	13.0	585	0.0087	0.9989	0.9989	0.9989	0.9989
0.0003	14.0	630	0.0088	0.9989	0.9989	0.9989	0.9989
0.0003	15.0	675	0.0089	0.9989	0.9989	0.9989	0.9989
0.0003	16.0	720	0.0090	0.9989	0.9989	0.9989	0.9989
0.0002	17.0	765	0.0090	0.9989	0.9989	0.9989	0.9989
0.0002	18.0	810	0.0090	0.9989	0.9989	0.9989	0.9989
0.0002	19.0	855	0.0091	0.9989	0.9989	0.9989	0.9989
0.0002	20.0	900	0.0091	0.9989	0.9989	0.9989	0.9989

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1