This model is a hybrid fine-tuned version of distilbert-base-uncased on Reddit dataset contains text related to mental health reports of users. it predicts mental health disorders from textual content.
It achieves the following results on the validation set:
- Loss: 0.1873
- F1: 0.6797
- AUC: 0.7942
- Precision: 0.7731
Description
This model is a finetuned BERT (bert-base-uncased) model that predict different mental disorders.
- It is trained on a costume dataset of texts or posts (from Reddit) about general experiences of users with mental health problems.
- Dataset was cleaned and all direct mentions of the disorder names in the texts were removed.
It includes the following classes:
- Borderline
- Anxiety
- Depression
- Bipolar
- OCD
- ADHD
- Schizophrenia
- Asperger
- PTSD
Training
Train size: 90%
Val size: 10%
Training set class counts (text samples) after balancing:
Borderline: 10398
Anxiety: 10393
Depression: 10400
Bipolar: 10359
OCD: 10413
ADHD: 10412
Schizophrenia: 10447
Asperger: 10470
PTSD: 10489
Validation set class counts after balancing:
Borderline: 1180
Anxiety: 1185
Depression: 1178
Bipolar: 1219
OCD: 1165
ADHD: 1166
Schizophrenia: 1131
Asperger: 1108
PTSD: 1089
model-finetuning: bert-base-uncased
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 32
val_batch_size: 32
optimizer: AdamW
num_epochs: 2-3
Training results
Epoch | Training Loss | Validation Loss |
---|---|---|
1.0 | 0.2089 | 0.1771 |
2.0 | 0.1525 | 0.1716 |
F1 Score: 0.6797
AUC Score: 0.7942
Classification Report
Borderline:
Precision: 0.6682
Recall: 0.5923
F1-score: 0.6280
Anxiety:
Precision: 0.6620
Recall: 0.6497
F1-score: 0.6558
Depression:
Precision: 0.7261
Recall: 0.5424
F1-score: 0.6209
Bipolar:
Precision: 0.8055
Recall: 0.5233
F1-score: 0.6345
OCD:
Precision: 0.8200
Recall: 0.6532
F1-score: 0.7271
ADHD:
Precision: 0.8740
Recall: 0.6603
F1-score: 0.7523
Schizophrenia:
Precision: 0.8017
Recall: 0.6472
F1-score: 0.7162
Asperger:
Precision: 0.7368
Recall: 0.6570
F1-score: 0.6946
PTSD:
Precision: 0.8612
Recall: 0.5812
F1-score: 0.6940
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Evaluation results
- F1self-reported0.680
- AUCself-reported0.794