bert-fraud-classification-test-mass – AI Model by sandeshrajx | AlphaNeural AI

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sandeshrajx/
bert-fraud-classification-test-mass

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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-mass

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: 0.3963
F1: 0.8194
Precision: 0.8445
Val Accuracy: 0.8375

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: 44
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 88
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Val Accuracy
0.5197	0.1743	40	0.5468	0.7488	0.6907	0.7459
0.5208	0.3486	80	0.4667	0.7687	0.7890	0.7911
0.4235	0.5229	120	0.4351	0.7986	0.7898	0.8113
0.404	0.6972	160	0.4577	0.7972	0.7751	0.8066
0.3736	0.8715	200	0.4274	0.7914	0.8775	0.8240
0.419	1.0458	240	0.4058	0.7912	0.8737	0.8232
0.2701	1.2200	280	0.4075	0.8124	0.8393	0.8316
0.4345	1.3943	320	0.4246	0.8110	0.8088	0.8244
0.3258	1.5686	360	0.4023	0.7992	0.8788	0.8294
0.3938	1.7429	400	0.3945	0.8174	0.8447	0.8361
0.2529	1.9172	440	0.3963	0.8194	0.8445	0.8375

Framework versions

Transformers 4.46.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.0