bert-finetuned-ner4

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

• eval_loss: 0.0775
• eval_precision: 0.9251
• eval_recall: 0.9460
• eval_f1: 0.9354
• eval_accuracy: 0.9841
• eval_runtime: 9.2322
• eval_samples_per_second: 352.028
• eval_steps_per_second: 44.085
• epoch: 1.0
• step: 1756

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

Framework versions

• Transformers 4.41.2
• Pytorch 2.1.2
• Datasets 2.19.2
• Tokenizers 0.19.1