bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.

Model description

This model is used for NER token classification and utilizes the CoNLL-2003 dataset. In processing this model, we used dynamic padding.

Intended uses & limitations

This model can be used for token classification in the field of NER. Named Entity Recognition (NER) is a token classification task where each token in a sentence is labeled to identify named entities such as persons, organizations, locations, dates, and other predefined categories. The goal is to detect and classify these entities within unstructured text, which is essential for information extraction and various natural language understanding applications.

Training and evaluation data

conll2033

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

train_loss': 0.08724395379752214

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1