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:

• eval_loss: 0.0593
• eval_precision: 0.9293
• eval_recall: 0.9485
• eval_f1: 0.9388
• eval_accuracy: 0.9858
• eval_runtime: 120.5431
• eval_samples_per_second: 26.97
• eval_steps_per_second: 3.376
• epoch: 2.0
• step: 3512

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 4.19.2
• Pytorch 1.11.0+cpu
• Datasets 2.2.2
• Tokenizers 0.12.1