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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: 0.0591
Precision: 0.9286
Recall: 0.9482
F1: 0.9383
Accuracy: 0.9863

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
0.0782	1.0	1756	0.0806	0.9182	0.9335	0.9258	0.9801
0.0401	2.0	3512	0.0585	0.9235	0.9461	0.9347	0.9853
0.0253	3.0	5268	0.0591	0.9286	0.9482	0.9383	0.9863

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3

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Token Classification

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Model tree for gtxygyzb/bert-finetuned-ner

Base model

google-bert/bert-base-cased

Finetuned

(1911)

this model

Dataset used to train gtxygyzb/bert-finetuned-ner

Evaluation results

Precision on conll2003
validation set self-reported

0.929
Recall on conll2003
validation set self-reported

0.948
F1 on conll2003
validation set self-reported

0.938
Accuracy on conll2003
validation set self-reported

0.986

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