test_trainer_20240918_1411 – AI Model by sandeshrajx | AlphaNeural AI

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sandeshrajx/
test_trainer_20240918_1411

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text-classification

generated_from_trainer

google-bert/bert-base-multilingual-uncased

autotrain_compatible

endpoints_compatible

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bert-fraud-classification-test

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

Loss: 0.6431
F1: 0.7435
Precision: 0.6605
Val Accuracy: {'accuracy': 0.706}

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: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Val Accuracy
0.601	0.32	40	0.6152	0.7039	0.6597	{'accuracy': 0.682}
0.6332	0.64	80	0.6143	0.7068	0.6515	{'accuracy': 0.679}
0.4862	0.96	120	0.5791	0.7151	0.7137	{'accuracy': 0.714}
0.6297	1.28	160	0.6323	0.7281	0.6495	{'accuracy': 0.69}
0.6164	1.6	200	0.5010	0.7522	0.8345	{'accuracy': 0.774}
0.6333	1.92	240	0.5824	0.7310	0.6828	{'accuracy': 0.71}
0.4465	2.24	280	0.5335	0.7579	0.7695	{'accuracy': 0.761}
0.5342	2.56	320	0.5065	0.7644	0.8040	{'accuracy': 0.775}
0.5462	2.88	360	0.6431	0.7435	0.6605	{'accuracy': 0.706}

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.4.0+cu121
Datasets 3.0.0
Tokenizers 0.19.1