ner-bert – AI Model by textminr | AlphaNeural AI

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textminr/
ner-bert

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token-classification

generated_from_trainer

google-bert/bert-base-multilingual-cased

autotrain_compatible

endpoints_compatible

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ner-bert

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

Loss: 0.0002
Precision: 1.0
Recall: 0.9993
F1: 0.9997
Accuracy: 1.0000

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0005	0.1	250	0.0047	0.9998	0.9861	0.9929	0.9994
0.009	0.2	500	0.0041	0.9961	0.9864	0.9912	0.9994
0.0004	0.3	750	0.0024	0.9977	0.9895	0.9936	0.9995
0.0001	0.4	1000	0.0010	0.9984	0.9975	0.9980	0.9999
0.0001	0.51	1250	0.0008	1.0	0.9975	0.9987	0.9999
0.0001	0.61	1500	0.0005	1.0	0.9975	0.9987	0.9999
0.0003	0.71	1750	0.0003	1.0	0.9991	0.9995	1.0000
0.0001	0.81	2000	0.0002	1.0	0.9993	0.9997	1.0000
0.0	0.91	2250	0.0002	1.0	0.9993	0.9997	1.0000

Framework versions

Transformers 4.37.2
Pytorch 2.2.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0