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spam-distilbert-checkpoints – AI Model by Romyla | AlphaNeural AI
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Romyla
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spam-distilbert-checkpoints
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transformers
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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spam-distilbert-checkpoints
This model is a fine-tuned version of
distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0196
Accuracy: 0.9942
Precision: 1.0
Recall: 0.9538
F1: 0.9764
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: 32
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
Precision
Recall
F1
0.0648
1.0
259
0.0524
0.9845
0.9394
0.9394
0.9394
0.0256
2.0
518
0.0426
0.9865
0.9403
0.9545
0.9474
0.0356
3.0
777
0.0408
0.9903
0.9692
0.9545
0.9618
Framework versions
Transformers 5.13.1
Pytorch 2.11.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2
