The training, evaluation & testing data can be found under samsaara/medical_condition_classification of the 🤗 Datasets and the process itself can be found in the modeling.ipynb notebook.

By default, the dataset has train, test splits. train is then further divided into train, validation splits with 0.8, 0.2 ratio. Final results shown are on the test dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.8625	0.4329	2000	1.7199	0.6397
1.459	0.8658	4000	1.3696	0.6890
1.1737	1.2987	6000	1.2131	0.7172
1.042	1.7316	8000	1.1014	0.7329
0.8431	2.1645	10000	1.0322	0.7510
0.8012	2.5974	12000	0.9889	0.7587
0.7312	3.0303	14000	0.9497	0.7727
0.6561	3.4632	16000	0.9338	0.7805
0.6132	3.8961	18000	0.9073	0.7875
0.5195	4.3290	20000	0.9011	0.7929
0.5015	4.7619	22000	0.8930	0.7951

Framework versions

Transformers 4.45.2
Pytorch 2.4.1
Datasets 3.0.1
Tokenizers 0.20.1