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BERT-cyberbullying-classifier-DirectFeatures – AI Model by rngrye | AlphaNeural AI
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BERT-cyberbullying-classifier-DirectFeatures
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transformers
safetensors
bert
generated_from_trainer
google-bert/bert-base-cased
finetune
apache-2.0
endpoints_compatible
us
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BERT-cyberbullying-classifier-DirectFeatures
This model is a fine-tuned version of
bert-base-cased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2471
Accuracy: 0.9010
F1: 0.8939
Precision: 0.8993
Recall: 0.9010
Auc: 0.9337
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: 16
seed: 22002423
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
Auc
0.2739
1.0
541
0.2423
0.9047
0.8938
0.9129
0.9047
0.9284
0.2137
2.0
1082
0.2471
0.9010
0.8939
0.8993
0.9010
0.9337
Framework versions
Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1
