In this SMS classification example, the first message is labeled as "label_0" because it appears to be a legitimate text message (ham) with someone informing they will arrive shortly. The second message is labeled as "label_1" because it is clearly spam, offering a prize and urging the recipient to click a link, which is a common characteristic of spam messages. The classification model uses these labels to identify and filter out spammy SMS messages, ensuring that legitimate messages reach the user's inbox (ham).

Training procedure

Colab

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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score
No log	1.0	244	0.1070	0.9785	0.9785
No log	2.0	488	0.0673	0.9880	0.9880
0.0885	3.0	732	0.0293	0.9946	0.9946
0.0885	4.0	976	0.0280	0.9964	0.9964
0.0306	5.0	1220	0.0355	0.9952	0.9952
0.0306	6.0	1464	0.0364	0.9952	0.9952
0.0087	7.0	1708	0.0448	0.9946	0.9946
0.0087	8.0	1952	0.0618	0.9922	0.9922
0.0047	9.0	2196	0.0420	0.9946	0.9946
0.0047	10.0	2440	0.0393	0.9946	0.9946

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

Sanrove/albert-spam-sms-classification-finetuned

training_dir

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Sanrove/
albert-spam-sms-classification-finetuned