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clinical-ner

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metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: clinical-ner
    results: []

clinical-ner

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8058
  • Precision: 0.5786
  • Recall: 0.6683
  • F1: 0.6202
  • Accuracy: 0.8099

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: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 45
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 5 4.7713 0.0002 0.001 0.0004 0.0182
No log 2.0 10 4.2264 0.0002 0.0008 0.0003 0.1481
No log 3.0 15 3.6238 0.0004 0.0003 0.0003 0.4575
4.2324 4.0 20 2.8751 0.0 0.0 0.0 0.4734
4.2324 5.0 25 2.4550 0.0306 0.0008 0.0015 0.4739
4.2324 6.0 30 2.1920 0.0722 0.0437 0.0545 0.5007
4.2324 7.0 35 1.9841 0.1137 0.1087 0.1112 0.5392
2.3521 8.0 40 1.8153 0.1956 0.189 0.1922 0.5829
2.3521 9.0 45 1.6504 0.2539 0.2617 0.2578 0.6218
2.3521 10.0 50 1.4801 0.3607 0.3787 0.3695 0.6782
2.3521 11.0 55 1.3417 0.3933 0.433 0.4122 0.7021
1.6185 12.0 60 1.2333 0.4054 0.4795 0.4394 0.7203
1.6185 13.0 65 1.1490 0.4307 0.5125 0.4680 0.7347
1.6185 14.0 70 1.0750 0.4412 0.543 0.4868 0.7503
1.6185 15.0 75 1.0179 0.4816 0.5637 0.5195 0.7619
1.1438 16.0 80 0.9774 0.4899 0.578 0.5303 0.7689
1.1438 17.0 85 0.9475 0.5005 0.5955 0.5439 0.7743
1.1438 18.0 90 0.9192 0.5082 0.6078 0.5535 0.7788
1.1438 19.0 95 0.8923 0.5151 0.6085 0.5579 0.7828
0.8863 20.0 100 0.8691 0.5263 0.6242 0.5711 0.7882
0.8863 21.0 105 0.8604 0.5358 0.6342 0.5809 0.7907
0.8863 22.0 110 0.8474 0.5429 0.641 0.5879 0.7946
0.8863 23.0 115 0.8362 0.5493 0.644 0.5929 0.7969
0.7361 24.0 120 0.8284 0.5531 0.6512 0.5982 0.7994
0.7361 25.0 125 0.8325 0.5555 0.6565 0.6018 0.8001
0.7361 26.0 130 0.8156 0.5686 0.6562 0.6093 0.8035
0.7361 27.0 135 0.8177 0.5634 0.6625 0.6089 0.8039
0.6449 28.0 140 0.8152 0.5643 0.6567 0.6070 0.8036
0.6449 29.0 145 0.8109 0.5700 0.6647 0.6137 0.8066
0.6449 30.0 150 0.8164 0.5697 0.6653 0.6138 0.8055
0.6449 31.0 155 0.8081 0.5742 0.6627 0.6153 0.8085
0.5912 32.0 160 0.8130 0.5687 0.6677 0.6142 0.8067
0.5912 33.0 165 0.8048 0.5779 0.6637 0.6179 0.8089
0.5912 34.0 170 0.8096 0.5760 0.669 0.6190 0.8085
0.5912 35.0 175 0.8063 0.5790 0.6677 0.6202 0.8091
0.5625 36.0 180 0.8052 0.5755 0.6673 0.6180 0.8094
0.5625 37.0 185 0.8063 0.5753 0.6667 0.6176 0.8093
0.5625 38.0 190 0.8055 0.5783 0.6677 0.6198 0.8103
0.5625 39.0 195 0.8052 0.5792 0.668 0.6205 0.8099
0.5442 40.0 200 0.8052 0.5798 0.6685 0.6210 0.8097
0.5442 41.0 205 0.8055 0.5784 0.6683 0.6201 0.8098
0.5442 42.0 210 0.8056 0.5789 0.6685 0.6205 0.8100
0.5442 43.0 215 0.8057 0.5786 0.6683 0.6202 0.8100
0.5397 44.0 220 0.8057 0.5786 0.6683 0.6202 0.8099
0.5397 45.0 225 0.8058 0.5786 0.6683 0.6202 0.8099

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1