CeLLaTe-ner-3class-bioformer8l-baseline

This model is a fine-tuned version of bioformers/bioformer-8l on the OTAR3088/CeLLaTe-ner-3class-iob_final dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0904
  • Precision: 0.7620
  • Recall: 0.7251
  • Micro F1: 0.7431
  • Weighted F1: 0.7414
  • Macro F1: 0.7292
  • Accuracy: 0.9796

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: 16
  • eval_batch_size: 16
  • seed: 3407
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • 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
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall Micro F1 Weighted F1 Macro F1 Accuracy
0.368 1.0 263 0.1009 0.4522 0.4864 0.4687 0.4498 0.4181 0.9666
0.0766 2.0 526 0.0744 0.6974 0.6853 0.6913 0.6926 0.6786 0.9761
0.0477 3.0 789 0.0741 0.7137 0.7148 0.7143 0.7116 0.7009 0.9776
0.0353 4.0 1052 0.0792 0.7014 0.6475 0.6734 0.6726 0.6556 0.9762
0.0269 5.0 1315 0.0818 0.6886 0.7097 0.6990 0.6994 0.6930 0.9767
0.0211 6.0 1578 0.0860 0.6955 0.6837 0.6896 0.6905 0.6836 0.9768
0.0177 7.0 1841 0.0887 0.7186 0.7271 0.7228 0.7230 0.7147 0.9777
0.0149 8.0 2104 0.0911 0.7620 0.7251 0.7431 0.7414 0.7292 0.9796
0.0121 9.0 2367 0.0890 0.7216 0.7515 0.7363 0.7352 0.7230 0.9785
0.0103 10.0 2630 0.1008 0.7319 0.7334 0.7326 0.7311 0.7183 0.9782
0.0091 11.0 2893 0.1026 0.7439 0.7184 0.7309 0.7297 0.7158 0.9789
0.0086 12.0 3156 0.1044 0.7168 0.6999 0.7083 0.7089 0.6957 0.9780
0.0072 13.0 3419 0.1105 0.7286 0.7148 0.7217 0.7215 0.7092 0.9781

Framework versions

  • Transformers 4.48.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.2
  • Tokenizers 0.21.0
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