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RoBERTa-WebAttack – AI Model by maleke01 | AlphaNeural AI
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maleke01
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RoBERTa-WebAttack
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transformers
tensorboard
safetensors
roberta
text-classification
generated_from_trainer
FacebookAI/roberta-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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RoBERTa-WebAttack
This model is a fine-tuned version of
roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0133
F1: 0.9974
Accuracy: 0.9974
Precision: 0.9974
Recall: 0.9974
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: 5e-05
train_batch_size: 48
eval_batch_size: 48
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
F1
Accuracy
Precision
Recall
0.0207
1.0
3713
0.0229
0.9956
0.9956
0.9956
0.9956
0.0215
2.0
7426
0.0158
0.9963
0.9963
0.9963
0.9963
0.001
3.0
11139
0.0133
0.9974
0.9974
0.9974
0.9974
Framework versions
Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1
