BERT Tiny fine tuned on Enron Spam Detection This model is a fine tuned version of google/bert uncased L 2 H 128 A 2 (aka BERT Tiny) on an SetFit/enron spam for Spam Dectection downstream task. It achieves the following results on the evaluation set: Loss: 0.0593 Precision: 0.9851 Recall: 0.9871 Accuracy: 0.986 F1: 0.9861 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: Adam with betas=(0.9,0.999) and epsilon=1e 08 lr scheduler type: linear num epochs: 4 Training results Training Loss Epoch Step Validation Loss Precision Recall Accuracy F1 : : : : : : : : : : : : : : : : 0.1125 1.0 1983 0.0797 0.9839 0.9692 0.9765 0.9765 0.061 2.0 3966 0.0618 0.9822 0.9861 0.984 0.9842 0.0486 3.0 5949 0.0593 0.9851 0.9871 0.986 0.9861 0.048 4.0 7932 0.0588 0.9870 0.9821 0.9845 0.9846 Framework versions Transformers 4.23.1 Pytorch 1.12.1+cu113 Datasets 2.6.1 Tokenizers 0.13.1
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