bert-meme-classifier – AI Model by musadiqpasha | AlphaNeural AI

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musadiqpasha/
bert-meme-classifier

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-meme-classifier

This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0546
Accuracy: 0.46
Auc: 0.636

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: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Auc
1.0854	1.0	560	1.0647	0.43	0.619
1.0549	2.0	1120	1.0566	0.453	0.62
1.0372	3.0	1680	1.0591	0.421	0.627
1.0232	4.0	2240	1.0514	0.46	0.633
1.0203	5.0	2800	1.0655	0.439	0.634
1.0237	6.0	3360	1.0529	0.469	0.636
1.006	7.0	3920	1.0576	0.442	0.635
1.0035	8.0	4480	1.0529	0.444	0.637
0.9947	9.0	5040	1.0544	0.447	0.635
0.9897	10.0	5600	1.0546	0.46	0.636

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1