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bert-finetuned-ner – AI Model by Ciphur | AlphaNeural AI
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Ciphur
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bert-finetuned-ner
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transformers
tensorboard
safetensors
bert
token-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert-finetuned-ner
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.2183
Precision: 0.6047
Recall: 0.6849
F1: 0.6423
Accuracy: 0.9285
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
Precision
Recall
F1
Accuracy
No log
1.0
125
0.2542
0.5559
0.6360
0.5933
0.9218
No log
2.0
250
0.2213
0.6
0.6733
0.6345
0.9268
No log
3.0
375
0.2183
0.6047
0.6849
0.6423
0.9285
Framework versions
Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0
