bert-finetuned-ner – AI Model by cli26 | AlphaNeural AI

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cli26/
bert-finetuned-ner

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token-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5678
Precision: 0.6565
Recall: 0.6991
F1: 0.6771
F1 brand: 0.3667
F1 food/drink: 0.6280
F1 quantity: 0.8571
F1 supplement: 0.0
Accuracy: 0.8485

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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	F1 brand	F1 food/drink	F1 quantity	F1 supplement	Accuracy
No log	1.0	55	0.6598	0.5654	0.6204	0.5916	0.0	0.5063	0.8177	0.0	0.7871
No log	2.0	110	0.5467	0.5983	0.6481	0.6222	0.2745	0.5571	0.8229	0.0	0.8157
No log	3.0	165	0.5237	0.6364	0.6806	0.6577	0.3548	0.6117	0.8391	0.0	0.8347
No log	4.0	220	0.5495	0.6371	0.6991	0.6667	0.3607	0.6168	0.8506	0.0	0.8443
No log	5.0	275	0.5361	0.7035	0.7361	0.7195	0.4918	0.6832	0.8571	0.0	0.8538
No log	6.0	330	0.5678	0.6565	0.6991	0.6771	0.3667	0.6280	0.8571	0.0	0.8485

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0