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distilbert-finetuned-ner

This model is a fine-tuned version of dslim/distilbert-NER on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4109
  • Precision: 0.6952
  • Recall: 0.7549
  • F1: 0.7238
  • Accuracy: 0.8724

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: 1e-06
  • 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: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
1.647 1.0 977 0.8265 0.4918 0.5741 0.5298 0.7793
0.7697 2.0 1954 0.6350 0.5801 0.6567 0.6160 0.8194
0.6089 3.0 2931 0.5591 0.6138 0.6857 0.6478 0.8352
0.534 4.0 3908 0.5163 0.6296 0.6955 0.6609 0.8439
0.4911 5.0 4885 0.4885 0.6436 0.7075 0.6740 0.8498
0.4545 6.0 5862 0.4683 0.6526 0.7165 0.6830 0.8557
0.4379 7.0 6839 0.4534 0.6600 0.7231 0.6901 0.8592
0.4124 8.0 7816 0.4441 0.6713 0.7274 0.6982 0.8625
0.403 9.0 8793 0.4345 0.6746 0.7359 0.7039 0.8658
0.394 10.0 9770 0.4324 0.6835 0.7445 0.7127 0.8667
0.3782 11.0 10747 0.4256 0.6820 0.7465 0.7128 0.8678
0.3706 12.0 11724 0.4213 0.6873 0.7460 0.7155 0.8691
0.3712 13.0 12701 0.4197 0.6873 0.7518 0.7181 0.8703
0.3626 14.0 13678 0.4163 0.6882 0.7523 0.7188 0.8713
0.351 15.0 14655 0.4142 0.6905 0.7528 0.7203 0.8717
0.3528 16.0 15632 0.4142 0.6932 0.7538 0.7222 0.8718
0.3523 17.0 16609 0.4123 0.6949 0.7533 0.7229 0.8722
0.3464 18.0 17586 0.4107 0.6936 0.7538 0.7224 0.8727
0.342 19.0 18563 0.4115 0.6954 0.7560 0.7244 0.8726
0.3496 20.0 19540 0.4109 0.6952 0.7549 0.7238 0.8724

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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