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moderate_severe_depression_model
This model is a fine-tuned version of allenai/longformer-scico on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4276
- Macro F1: 0.8927
- Accuracy: 0.8932
Results on Test set:
-Accuracy: 0.8817204301075269
-F1 score: 0.8819253137510324
-Precision: 0.8855477220587717
-Recall : 0.8817204301075269
-Matthews Correlation Coefficient: 0.8242972089300715
-Precision of each class: [0.98420129 0.85636693 0.81607495]
-Recall of each class: [0.93548387 0.78921023 0.92046719]
-F1 score of each class: [0.95922441 0.82141823 0.8651333 ]
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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy |
|---|---|---|---|---|---|
| 0.3476 | 1.0 | 1798 | 0.3343 | 0.8765 | 0.8782 |
| 0.2658 | 2.0 | 3596 | 0.3190 | 0.8856 | 0.8859 |
| 0.2157 | 3.0 | 5394 | 0.3607 | 0.8938 | 0.8939 |
| 0.1749 | 4.0 | 7192 | 0.4276 | 0.8927 | 0.8932 |
Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3
