bert-base-uncased-en-ner

This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1434
• Precision: 0.9001
• Recall: 0.9096
• F1: 0.9048
• Accuracy: 0.9772

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

The model was trained on data that follows the IOB convention. Full tagset with indices:

{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 4e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 0
• 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
0.0759	1.0	1756	0.1246	0.8878	0.8973	0.8925	0.9744
0.0299	2.0	3512	0.1427	0.8911	0.9040	0.8975	0.9749
0.0152	3.0	5268	0.1434	0.9001	0.9096	0.9048	0.9772

Framework versions

• Transformers 4.27.2
• Pytorch 1.13.1+cu116
• Datasets 2.10.1
• Tokenizers 0.13.2