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bert-conll2003-ner – AI Model by rcd12 | AlphaNeural AI
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rcd12
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bert-conll2003-ner
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transformers
safetensors
bert
token-classification
generated_from_trainer
google-bert/bert-base-cased
finetune
apache-2.0
endpoints_compatible
us
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bert-conll2003-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: 0.1154
Precision: 0.9026
Recall: 0.9210
F1: 0.9117
Accuracy: 0.9827
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: 16
eval_batch_size: 16
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
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
0.0456
1.0
878
0.0421
0.9238
0.9340
0.9289
0.9886
0.0206
2.0
1756
0.0388
0.9450
0.9490
0.9470
0.9907
0.0131
3.0
2634
0.0371
0.9435
0.9517
0.9476
0.9913
Framework versions
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2
