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bert_chinese_mc_base-BioNER-EN-ZH

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metadata
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert_chinese_mc_base-BioNER-EN-ZH
    results: []

bert_chinese_mc_base-BioNER-EN-ZH

This model is a fine-tuned version of StivenLancheros/bert_chinese_mc_base-BioNER-EN on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3611
  • Precision: 0.6967
  • Recall: 0.7980
  • F1: 0.7439
  • Accuracy: 0.9215

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
0.4895 1.0 680 0.6248 0.4389 0.6486 0.5235 0.8301
0.3569 2.0 1360 0.6207 0.4931 0.7204 0.5854 0.8481
0.2778 3.0 2040 0.4876 0.5723 0.7371 0.6443 0.8864
0.2558 4.0 2720 0.4496 0.5882 0.7446 0.6572 0.8892
0.2363 5.0 3400 0.4674 0.5845 0.7619 0.6615 0.8892
0.2129 6.0 4080 0.4311 0.6148 0.7674 0.6827 0.9005
0.2019 7.0 4760 0.3930 0.6428 0.7710 0.7011 0.9103
0.1912 8.0 5440 0.4031 0.6438 0.7815 0.7060 0.9095
0.1741 9.0 6120 0.3914 0.6506 0.7765 0.7080 0.9101
0.1727 10.0 6800 0.3808 0.6530 0.7814 0.7114 0.9117
0.1625 11.0 7480 0.4047 0.6545 0.7828 0.7129 0.9106
0.1546 12.0 8160 0.3803 0.6543 0.7849 0.7137 0.9115
0.1515 13.0 8840 0.3635 0.6828 0.7979 0.7359 0.9217
0.1415 14.0 9520 0.3872 0.6718 0.7962 0.7287 0.9160
0.1425 15.0 10200 0.3699 0.6879 0.7939 0.7371 0.9193
0.1327 16.0 10880 0.3762 0.6869 0.7977 0.7382 0.9184
0.1307 17.0 11560 0.3732 0.6822 0.8013 0.7369 0.9181
0.1309 18.0 12240 0.3629 0.6956 0.7970 0.7428 0.9208
0.1268 19.0 12920 0.3643 0.6930 0.7990 0.7423 0.9210
0.1257 20.0 13600 0.3611 0.6967 0.7980 0.7439 0.9215

Framework versions

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