sms-spam-weighted – AI Model by cesullivan99 | AlphaNeural AI

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sms-spam-weighted

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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sms-spam-weighted

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: 0.2336
Accuracy: 0.989
F1: 0.9575

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: 8
eval_batch_size: 8
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
0.0009	1.0	125	0.1323	0.987	0.9494
0.0034	2.0	250	0.1401	0.988	0.9531
0.0001	3.0	375	0.2087	0.991	0.9647
0.0001	4.0	500	0.2121	0.988	0.9538
0.0001	5.0	625	0.2129	0.988	0.9538
0.0	6.0	750	0.2242	0.99	0.9612
0.0	7.0	875	0.2285	0.989	0.9575
0.0	8.0	1000	0.2314	0.989	0.9575
0.0	9.0	1125	0.2330	0.989	0.9575
0.0	10.0	1250	0.2336	0.989	0.9575

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3