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distilbert-spam-detector – AI Model by toporaku | AlphaNeural AI
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toporaku
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distilbert-spam-detector
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transformers
safetensors
distilbert
text-classification
spam-detection
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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distilbert-spam-detector
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.0556
Accuracy: 0.9857
F1: 0.9459
Precision: 0.9589
Recall: 0.9333
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: 32
eval_batch_size: 64
seed: 42
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: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
No log
1.0
140
0.0197
0.9964
0.9863
1.0
0.9730
No log
2.0
280
0.0165
0.9964
0.9863
1.0
0.9730
No log
3.0
420
0.0170
0.9964
0.9863
1.0
0.9730
Framework versions
Transformers 5.0.0
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2
