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Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC

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Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC / README.md
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metadata
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC
    results: []

Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC

This model is a fine-tuned version of StivenLancheros/Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE2-NoEWC on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4245
  • Precision: 0.7158
  • Recall: 0.7763
  • F1: 0.7448
  • Accuracy: 0.9230

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • 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
0.4287 1.0 1360 0.4776 0.6125 0.7809 0.6865 0.8944
0.274 2.0 2720 0.4727 0.6370 0.7705 0.6974 0.9026
0.2219 3.0 4080 0.4405 0.6790 0.7695 0.7215 0.9134
0.1964 4.0 5440 0.4245 0.7158 0.7763 0.7448 0.9230
0.1599 5.0 6800 0.4378 0.7029 0.7732 0.7364 0.9204
0.1489 6.0 8160 0.4324 0.7018 0.7749 0.7365 0.9196
0.1342 7.0 9520 0.4534 0.7131 0.7867 0.7481 0.9216
0.12 8.0 10880 0.4532 0.7230 0.7865 0.7534 0.9243
0.1135 9.0 12240 0.4688 0.7155 0.7705 0.7420 0.9206
0.107 10.0 13600 0.4713 0.7214 0.7863 0.7524 0.9241
0.1004 11.0 14960 0.4594 0.7278 0.7823 0.7541 0.9251
0.093 12.0 16320 0.4564 0.7343 0.7937 0.7628 0.9275
0.0862 13.0 17680 0.4699 0.7242 0.7970 0.7588 0.9262
0.0788 14.0 19040 0.4745 0.7397 0.7935 0.7657 0.9285
0.0785 15.0 20400 0.4822 0.7389 0.7998 0.7682 0.9283
0.0711 16.0 21760 0.5031 0.7255 0.7964 0.7593 0.9264
0.073 17.0 23120 0.5038 0.7317 0.7976 0.7632 0.9274
0.0695 18.0 24480 0.4860 0.7411 0.8016 0.7702 0.9296
0.0649 19.0 25840 0.4978 0.7376 0.7977 0.7665 0.9285
0.0605 20.0 27200 0.4983 0.7371 0.7974 0.7661 0.9286

Framework versions

  • Transformers 4.27.2
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1
  • Tokenizers 0.13.2