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adr-ner

This model is a fine-tuned version of austin/Austin-MeDeBERTa on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0434
  • Precision: 0.7305
  • Recall: 0.6934
  • F1: 0.7115
  • Accuracy: 0.9941

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: 5e-05
  • train_batch_size: 12
  • eval_batch_size: 12
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 107 0.0630 0.0 0.0 0.0 0.9876
No log 2.0 214 0.0308 0.4282 0.3467 0.3832 0.9900
No log 3.0 321 0.0254 0.5544 0.5603 0.5573 0.9920
No log 4.0 428 0.0280 0.6430 0.5751 0.6071 0.9929
0.0465 5.0 535 0.0266 0.5348 0.7146 0.6118 0.9915
0.0465 6.0 642 0.0423 0.7632 0.5793 0.6587 0.9939
0.0465 7.0 749 0.0336 0.6957 0.6765 0.6860 0.9939
0.0465 8.0 856 0.0370 0.6876 0.6702 0.6788 0.9936
0.0465 9.0 963 0.0349 0.6555 0.7040 0.6789 0.9932
0.0044 10.0 1070 0.0403 0.6910 0.6808 0.6858 0.9938
0.0044 11.0 1177 0.0415 0.7140 0.6808 0.6970 0.9939
0.0044 12.0 1284 0.0440 0.7349 0.6681 0.6999 0.9941
0.0044 13.0 1391 0.0423 0.7097 0.6977 0.7036 0.9941
0.0044 14.0 1498 0.0435 0.7174 0.6977 0.7074 0.9941
0.0006 15.0 1605 0.0434 0.7305 0.6934 0.7115 0.9941

Framework versions

  • Transformers 4.14.1
  • Pytorch 1.10.0+cu113
  • Datasets 1.16.1
  • Tokenizers 0.10.3
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