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bert-finetuned-ner – AI Model by HymanRoth | AlphaNeural AI
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HymanRoth
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bert-finetuned-ner
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transformers
tensorboard
safetensors
bert
token-classification
generated_from_trainer
google-bert/bert-base-cased
finetune
apache-2.0
endpoints_compatible
us
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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: 0.0644
Precision: 0.9356
Recall: 0.9517
F1: 0.9436
Accuracy: 0.9864
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: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
0.0755
1.0
1756
0.0688
0.9075
0.9340
0.9206
0.9816
0.0361
2.0
3512
0.0716
0.9314
0.9455
0.9384
0.9853
0.0218
3.0
5268
0.0644
0.9356
0.9517
0.9436
0.9864
Framework versions
Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0
