bert-tiny-finetuned-enron-spam-detection – AI Model by mrm8488 | AlphaNeural AI

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mrm8488/
bert-tiny-finetuned-enron-spam-detection

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text-classification

generated_from_trainer

SetFit/enron_spam

autotrain_compatible

endpoints_compatible

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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