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distilbert-base-uncased-finetuned-spam-detection-dataset-splits – AI Model by Chris267 | AlphaNeural AI
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Chris267
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distilbert-base-uncased-finetuned-spam-detection-dataset-splits
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transformers
tensorboard
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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distilbert-base-uncased-finetuned-spam-detection-dataset-splits
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: 0.0201
Accuracy: 0.9963
F1: 0.9963
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: 64
eval_batch_size: 64
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
0.0978
1.0
128
0.0203
0.9971
0.9971
0.0051
2.0
256
0.0201
0.9963
0.9963
Framework versions
Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3
