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log-anomaly-detection-model-roberta – AI Model by Dumi2025 | AlphaNeural AI
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Dumi2025
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log-anomaly-detection-model-roberta
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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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log-anomaly-detection-model-roberta
This model is a fine-tuned version of
FacebookAI/roberta-base
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.1524
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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
0.183
1.0
10482
0.1558
0.0091
2.0
20964
0.1585
0.1212
3.0
31446
0.1524
0.0653
4.0
41928
0.1524
0.3581
5.0
52410
0.1545
Framework versions
Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0
