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moderate_severe_depression_model – AI Model by christinacdl | AlphaNeural AI
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christinacdl
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moderate_severe_depression_model
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transformers
pytorch
tensorboard
longformer
text-classification
generated_from_trainer
code
en
christinacdl/balanced_depression_dataset
apache-2.0
autotrain_compatible
endpoints_compatible
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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
