Bert-NER – AI Model by Kriyans | AlphaNeural AI

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Kriyans/
Bert-NER

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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Bert-NER

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

Loss: 0.1205
Precision: 0.9752
Recall: 0.9924
F1: 0.9837
Accuracy: 0.9730

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0825	1.0	501	0.1031	0.9600	0.9917	0.9756	0.9770
0.0337	2.0	1002	0.1491	0.9615	0.9942	0.9776	0.9648
0.0285	3.0	1503	0.1169	0.9754	0.9913	0.9833	0.9723
0.0249	4.0	2004	0.1054	0.9724	0.9921	0.9821	0.9783
0.0232	5.0	2505	0.1205	0.9752	0.9924	0.9837	0.9730

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1