mental_classification

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

• Loss: 0.6424
• Accuracy: 0.8623

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: 14

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.356	1.4046	184	1.6835	0.5908
1.2119	2.8092	368	1.1011	0.7648
0.6548	4.2137	552	0.8192	0.8241
0.3782	5.6183	736	0.6968	0.8375
0.1931	7.0229	920	0.6587	0.8528
0.1127	8.4275	1104	0.6390	0.8566
0.081	9.8321	1288	0.6382	0.8566
0.0532	11.2366	1472	0.6433	0.8623
0.0416	12.6412	1656	0.6424	0.8623

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1