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bert-finetuned-ner – AI Model by Rtariq | AlphaNeural AI
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Rtariq
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bert-finetuned-ner
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transformers
pytorch
tensorboard
bert
token-classification
generated_from_trainer
conll2003
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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bert-finetuned-ner
This model is a fine-tuned version of
bert-base-cased
on the conll2003 dataset. It achieves the following results on the evaluation set:
Loss: 0.0637
Precision: 0.9280
Recall: 0.9475
F1: 0.9376
Accuracy: 0.9858
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
0.0866
1.0
1756
0.0714
0.9116
0.9300
0.9207
0.9821
0.0341
2.0
3512
0.0672
0.9284
0.9468
0.9375
0.9853
0.019
3.0
5268
0.0637
0.9280
0.9475
0.9376
0.9858
Framework versions
Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2
