mental_bert_classifier

This model is a fine-tuned version of Zamoranesis/mental_bert on hackathon-somos-nlp-2023/DiagTrast. It achieves the following results on the evaluation set:

• Loss: 0.2426
• F1 Class 0: 0.8852
• F1 Class 1: 0.9512
• F1 Class 2: 0.8421
• F1 Class 3: 0.8539
• F1 Class 4: 0.9412
• F1: 0.8947

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: 0.005
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• lr_scheduler_warmup_steps: 100
• training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Class 0	F1 Class 1	F1 Class 2	F1 Class 3	F1 Class 4	F1
0.7979	6.25	100	0.3557	0.8421	0.9512	0.8214	0.8889	0.9091	0.8825
0.2559	12.5	200	0.2823	0.9333	0.9412	0.8364	0.8602	0.9362	0.9015
0.1963	18.75	300	0.2610	0.9180	0.9756	0.7778	0.8352	0.9231	0.8859
0.1717	25.0	400	0.2534	0.9180	0.9630	0.8	0.8261	0.9412	0.8897
0.1511	31.25	500	0.2476	0.8667	0.9512	0.8148	0.8298	0.96	0.8845
0.1501	37.5	600	0.2513	0.9	0.9630	0.8	0.8261	0.9231	0.8824
0.1427	43.75	700	0.2581	0.9180	0.9756	0.8475	0.8810	0.8889	0.9022
0.1457	50.0	800	0.2428	0.8852	0.9512	0.8	0.8261	0.92	0.8765
0.1311	56.25	900	0.2462	0.9	0.9512	0.8	0.8352	0.9231	0.8819
0.1346	62.5	1000	0.2426	0.8852	0.9512	0.8421	0.8539	0.9412	0.8947

Framework versions

• Transformers 4.33.3
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3