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relation-biobert-biocause

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2103
  • Precision: 0.1164
  • Recall: 0.625
  • F1: 0.1963
  • Accuracy: 0.9448
  • Relation P: 0.1164
  • Relation R: 0.625
  • Relation F1: 0.1963

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: 4e-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: 1

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy Relation P Relation R Relation F1
0.6563 0.1282 20 0.2984 0.0211 0.2105 0.0384 0.8265 0.0211 0.2105 0.0384
0.6563 0.2564 40 0.2302 0.0763 0.4605 0.1308 0.9266 0.0763 0.4605 0.1308
0.6563 0.3846 60 0.4003 0.1406 0.5921 0.2273 0.9617 0.1406 0.5921 0.2273
0.6563 0.5128 80 0.2185 0.0554 0.4671 0.0990 0.8933 0.0554 0.4671 0.0990
0.6563 0.6410 100 0.2261 0.1345 0.7105 0.2262 0.9510 0.1345 0.7105 0.2262
0.6563 0.7692 120 0.2315 0.1259 0.6579 0.2114 0.9502 0.1259 0.6579 0.2114
0.6563 0.8974 140 0.2324 0.1417 0.6711 0.2339 0.9547 0.1417 0.6711 0.2339

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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