text-classification-medical

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

• Loss: 0.0394
• Accuracy: 1.0
• F1: 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	13	0.6409	0.5714	0.7273
No log	2.0	26	0.5385	0.8297	0.8397
No log	3.0	39	0.3346	0.9286	0.9293
No log	4.0	52	0.1979	0.9780	0.9781
No log	5.0	65	0.1321	0.9945	0.9945
No log	6.0	78	0.0932	1.0	1.0
No log	7.0	91	0.0654	1.0	1.0
No log	8.0	104	0.0508	1.0	1.0
No log	9.0	117	0.0420	1.0	1.0
No log	10.0	130	0.0394	1.0	1.0

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0