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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: 0.0571
Precision: 0.9395
Recall: 0.9520
F1: 0.9457
Accuracy: 0.9869

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
0.0758	1.0	1756	0.0750	0.9073	0.9312	0.9191	0.9798
0.0388	2.0	3512	0.0562	0.9262	0.9460	0.9360	0.9859
0.0269	3.0	5268	0.0571	0.9395	0.9520	0.9457	0.9869

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.2
Tokenizers 0.13.3

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Token Classification

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Model tree for cssupport/bert-finetuned-ner

Base model

google-bert/bert-base-cased

Finetuned

(1948)

this model

Dataset used to train cssupport/bert-finetuned-ner

Evaluation results

Precision on conll2003
validation set self-reported

0.940
Recall on conll2003
validation set self-reported

0.952
F1 on conll2003
validation set self-reported

0.946
Accuracy on conll2003
validation set self-reported

0.987

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