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bert-finetuned-ner

Token Classification Transformers PyTorch TensorBoard

bert generated_from_trainer Eval Results AutoTrain Compatible License: apache-2.0

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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: 0.0634
Precision: 0.9327
Recall: 0.9500
F1: 0.9413
Accuracy: 0.9861

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
0.0876	1.0	1756	0.0692	0.9127	0.9355	0.9240	0.9819
0.0316	2.0	3512	0.0651	0.9284	0.9490	0.9386	0.9850
0.0215	3.0	5268	0.0634	0.9327	0.9500	0.9413	0.9861

Framework versions

Transformers 4.18.0
Pytorch 1.10.0+cu111
Datasets 2.1.0
Tokenizers 0.12.1

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Hosted inference API

Token Classification

Examples

This model can be loaded on the Inference API on-demand.

Dataset used to train ysharma/bert-finetuned-ner

Evaluation results

Precision on conll2003
self-reported

0.933
Recall on conll2003
self-reported

0.950
F1 on conll2003
self-reported

0.941
Accuracy on conll2003
self-reported

0.986

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