pii_bert_model – AI Model by abhinavsarkar | AlphaNeural AI

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abhinavsarkar/
pii_bert_model

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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pii_bert_model

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1020
Precision: 0.9748
Recall: 0.9744
F1: 0.9745

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: 16
eval_batch_size: 16
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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.1362	1.0	150	0.1090	0.9663	0.9642	0.9645
0.0974	2.0	300	0.1192	0.9632	0.9619	0.9620
0.0736	3.0	450	0.0867	0.9696	0.9693	0.9693
0.0605	4.0	600	0.1111	0.9696	0.9682	0.9685
0.0445	5.0	750	0.0895	0.9729	0.9726	0.9726
0.0343	6.0	900	0.0952	0.9727	0.9719	0.9721
0.023	7.0	1050	0.0991	0.9745	0.9740	0.9741
0.0179	8.0	1200	0.1020	0.9748	0.9744	0.9745

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1