distilbert-base-cased-ner

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

  • Loss: 0.1088
  • Precision: 0.9321
  • Recall: 0.9492
  • F1: 0.9405
  • Accuracy: 0.9848

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: 2147483647
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.1015 1.0 1756 0.1001 0.8858 0.9167 0.9010 0.9740
0.049 2.0 3512 0.0803 0.8993 0.9273 0.9131 0.9798
0.0327 3.0 5268 0.0794 0.9199 0.9350 0.9274 0.9821
0.0237 4.0 7024 0.0880 0.9050 0.9344 0.9194 0.9813
0.0131 5.0 8780 0.0849 0.9178 0.9446 0.9310 0.9837
0.0073 6.0 10536 0.0975 0.9166 0.9446 0.9304 0.9838
0.0044 7.0 12292 0.0965 0.9267 0.9475 0.9370 0.9842
0.0015 8.0 14048 0.1075 0.9273 0.9463 0.9367 0.9843
0.0011 9.0 15804 0.1089 0.9317 0.9480 0.9398 0.9847
0.0006 10.0 17560 0.1088 0.9321 0.9492 0.9405 0.9848

Framework versions

  • Transformers 4.27.4
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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Dataset used to train alvarobartt/distilbert-base-cased-ner

Evaluation results