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jessiexuening/
bert_finetuned_ner

Name: bert_finetuned_ner
Rating: 2.5 (5 reviews)

generated_from_trainer

google-bert/bert-base-cased

finetune

apache-2.0

endpoints_compatible

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bert_finetuned_ner

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

Loss: 0.0635
Loc: {'precision': 0.9579741379310345, 'recall': 0.9678824169842134, 'f1': 0.9629027890603845, 'number': 5970250}
Misc: {'precision': 0.8798328108672936, 'recall': 0.913232104121475, 'f1': 0.8962213943587015, 'number': 2996500}
Org: {'precision': 0.9199406968124537, 'recall': 0.9254287844891872, 'f1': 0.9226765799256506, 'number': 4358250}
Per: {'precision': 0.9654986522911051, 'recall': 0.9723127035830619, 'f1': 0.9688936975926427, 'number': 5986500}
Overall Precision: 0.9393
Overall Recall: 0.9512
Overall F1: 0.9452
Overall Accuracy: 0.9870

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: 8
eval_batch_size: 8
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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Loc	Misc	Org	Per	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0651	1.0	1756	0.0611	{'precision': 0.9290558163797601, 'recall': 0.9695155144256941, 'f1': 0.9488545551411828, 'number': 5970250}	{'precision': 0.8428128231644261, 'recall': 0.8839479392624728, 'f1': 0.8628904182106935, 'number': 2996500}	{'precision': 0.8939724037763254, 'recall': 0.9179716629381058, 'f1': 0.905813097866078, 'number': 4358250}	{'precision': 0.9567567567567568, 'recall': 0.9609120521172638, 'f1': 0.9588299024918744, 'number': 5986500}	0.9159	0.9419	0.9287	0.9841
0.0223	2.0	3512	0.0631	{'precision': 0.9636462289744981, 'recall': 0.9667936853565596, 'f1': 0.9652173913043479, 'number': 5970250}	{'precision': 0.8748685594111462, 'recall': 0.9023861171366594, 'f1': 0.8884143085958356, 'number': 2996500}	{'precision': 0.9205642167780252, 'recall': 0.9246830723340791, 'f1': 0.9226190476190476, 'number': 4358250}	{'precision': 0.9718462371413102, 'recall': 0.9744842562432139, 'f1': 0.9731634589319599, 'number': 5986500}	0.9424	0.9497	0.9460	0.9871
0.0129	3.0	5268	0.0635	{'precision': 0.9579741379310345, 'recall': 0.9678824169842134, 'f1': 0.9629027890603845, 'number': 5970250}	{'precision': 0.8798328108672936, 'recall': 0.913232104121475, 'f1': 0.8962213943587015, 'number': 2996500}	{'precision': 0.9199406968124537, 'recall': 0.9254287844891872, 'f1': 0.9226765799256506, 'number': 4358250}	{'precision': 0.9654986522911051, 'recall': 0.9723127035830619, 'f1': 0.9688936975926427, 'number': 5986500}	0.9393	0.9512	0.9452	0.9870

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

jessiexuening/bert_finetuned_ner

bert_finetuned_ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jessiexuening/
bert_finetuned_ner