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BERTForDetectingDepression-Twitter2020 – AI Model by Silicon23 | AlphaNeural AI
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Silicon23
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BERTForDetectingDepression-Twitter2020
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
AIMH/mental-bert-base-cased
finetune
cc-by-nc-4.0
autotrain_compatible
endpoints_compatible
us
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BERTForDetectingDepression-Twitter2020
This model is a fine-tuned version of
AIMH/mental-bert-base-cased
on data taken from
Safa, R., Bayat, P. & Moghtader, L. Automatic detection of depression symptoms in twitter using multimodal analysis. J Supercomput (2021).
. It achieves the following results on the evaluation set:
Loss: 0.8966
Accuracy: 0.6445
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Eval Accuracy: 0.6445
Eval Precision: 0.627281460134486
Eval Recall: 0.6690573770491803
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 3.083803249747333e-05
train_batch_size: 4
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.6484
1.0
4500
0.6851
0.637
0.5904
2.0
9000
0.8966
0.6445
Framework versions
Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1
