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bert-base-uncased-enron_spam – AI Model by cvnberk | AlphaNeural AI
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cvnberk
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bert-base-uncased-enron_spam
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transformers
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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bert-base-uncased-enron_spam
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0280
Accuracy: 0.9945
F1: 0.9945
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: 16
eval_batch_size: 64
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
Accuracy
F1
0.0109
1.0
992
0.0384
0.9935
0.9935
0.0787
2.0
1984
0.0280
0.9945
0.9945
0.0003
3.0
2976
0.0413
0.993
0.9930
Framework versions
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1
