bert-finetuned-spam – AI Model by ana-grassmann | AlphaNeural AI

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ana-grassmann/
bert-finetuned-spam

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google-bert/bert-base-uncased

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bert-finetuned-spam

This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1942
Accuracy: 0.952
F1: 0.9502
Precision: 0.9871
Recall: 0.916

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: 2.5719605731158755e-06
train_batch_size: 4
eval_batch_size: 2
seed: 19
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.2449	1.0	2250	0.2435	0.901	0.8930	0.9718	0.826
0.2545	2.0	4500	0.2138	0.937	0.9336	0.9866	0.886
0.1397	3.0	6750	0.2162	0.944	0.9413	0.9890	0.898
0.1184	4.0	9000	0.2134	0.946	0.9436	0.9869	0.904
0.2056	5.0	11250	0.1942	0.952	0.9502	0.9871	0.916

Framework versions

PEFT 0.11.1
Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1