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bert-finetuned-ner

Token Classification

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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: 0.0599
Precision: 0.9306
Recall: 0.9498
F1: 0.9401
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: 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
0.077	1.0	1756	0.0789	0.9094	0.9330	0.9211	0.9797
0.0401	2.0	3512	0.0587	0.9263	0.9460	0.9361	0.9858
0.0251	3.0	5268	0.0599	0.9306	0.9498	0.9401	0.9864

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

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Token Classification

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Model tree for chunwoolee0/bert-finetuned-ner

Base model

google-bert/bert-base-cased

Finetuned

(1931)

this model

Dataset used to train chunwoolee0/bert-finetuned-ner

Evaluation results

Precision on conll2003
validation set self-reported

0.931
Recall on conll2003
validation set self-reported

0.950
F1 on conll2003
validation set self-reported

0.940
Accuracy on conll2003
validation set self-reported

0.986

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