Symptoms_to_Diagnosis_SonatafyAI_BERT_v1

This model is a fine-tuned version of bert-base-uncased on the symptoms to diagnosis dataset. It achieves the following results on the evaluation set:

• Loss: 0.4088
• Accuracy: 0.9387

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
No log	1.0	54	2.7055	0.25
No log	2.0	108	2.1468	0.6792
No log	3.0	162	1.5608	0.8019
No log	4.0	216	1.1596	0.8632
No log	5.0	270	0.8834	0.8868
No log	6.0	324	0.6775	0.9104
No log	7.0	378	0.5516	0.9198
No log	8.0	432	0.4632	0.9434
No log	9.0	486	0.4273	0.9387
1.2941	10.0	540	0.4088	0.9387

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2