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reem1997/
depressionDetectionArabert

generated_from_trainer

autotrain_compatible

endpoints_compatible

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depressionDetectionArabert

This model is a fine-tuned version of aubmindlab/bert-base-arabertv02-twitter on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4586
Macro F1: 0.9279
Precision: 0.9281
Recall: 0.9279
Kappa: 0.8558
Accuracy: 0.9279

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: 128
seed: 25
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Macro F1	Precision	Recall	Kappa	Accuracy
No log	1.0	407	0.2285	0.9143	0.9181	0.9144	0.8289	0.9144
0.2459	2.0	815	0.2124	0.9264	0.9269	0.9264	0.8528	0.9264
0.1306	3.0	1222	0.2439	0.9285	0.9287	0.9285	0.8570	0.9285
0.0747	4.0	1630	0.3240	0.9270	0.9272	0.9270	0.8540	0.9270
0.0328	5.0	2037	0.3850	0.9273	0.9275	0.9273	0.8546	0.9273
0.0328	6.0	2445	0.4129	0.9279	0.9280	0.9279	0.8558	0.9279
0.0213	7.0	2852	0.4541	0.9273	0.9273	0.9273	0.8545	0.9273
0.0108	7.99	3256	0.4586	0.9279	0.9281	0.9279	0.8558	0.9279

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Tokenizers 0.13.3

reem1997/depressionDetectionArabert

depressionDetectionArabert

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

reem1997/
depressionDetectionArabert