NER-BERT – AI Model by hiudev | AlphaNeural AI

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hiudev/
NER-BERT

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token-classification

generated_from_trainer

dslim/bert-base-NER

autotrain_compatible

endpoints_compatible

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NER-BERT

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

Loss: 0.0000
Token Accuracy: 1.0000
Token Precision: 1.0000
Token Recall: 1.0000
Token F1: 1.0000
Entity Precision: 0.9999
Entity Recall: 0.9999
Entity F1: 0.9999

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: 2e-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
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Token Accuracy	Token Precision	Token Recall	Token F1	Entity Precision	Entity Recall	Entity F1
0.0004	1.0	2250	0.0002	1.0000	1.0000	1.0000	1.0000	0.9995	0.9996	0.9996
0.0003	2.0	4500	0.0001	1.0000	1.0000	1.0000	1.0000	0.9998	0.9999	0.9998
0.0001	3.0	6750	0.0000	1.0000	1.0000	1.0000	1.0000	0.9999	0.9999	0.9999

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1