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bert-base-uncased-finetuned-ner

Token Classification
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PyTorch
bert
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - generator
model-index:
  - name: bert-base-uncased-finetuned-ner
    results: []

bert-base-uncased-finetuned-ner

This model is a fine-tuned version of bert-base-uncased on the generator dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9799
  • Overall Precision: 0.4362
  • Overall Recall: 0.5285
  • Overall F1: 0.4779
  • Overall Accuracy: 0.9515
  • Datasetname F1: 0.3262
  • Hyperparametername F1: 0.7339
  • Hyperparametervalue F1: 0.7619
  • Methodname F1: 0.5193
  • Metricname F1: 0.6525
  • Metricvalue F1: 0.6452
  • Taskname F1: 0.2704

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Overall Precision Overall Recall Overall F1 Overall Accuracy Datasetname F1 Hyperparametername F1 Hyperparametervalue F1 Methodname F1 Metricname F1 Metricvalue F1 Taskname F1
No log 1.0 132 0.5733 0.2215 0.3313 0.2655 0.9350 0.0 0.1273 0.325 0.4369 0.2759 0.3478 0.0639
No log 2.0 264 0.4130 0.2929 0.5996 0.3936 0.9348 0.2930 0.3316 0.6133 0.5104 0.5622 0.5263 0.2452
No log 3.0 396 0.4654 0.3302 0.5793 0.4207 0.9431 0.2732 0.5 0.7463 0.5189 0.5930 0.5405 0.2228
0.5901 4.0 528 0.4540 0.3604 0.5772 0.4438 0.9457 0.2644 0.5271 0.7027 0.5346 0.6386 0.4878 0.2804
0.5901 5.0 660 0.5776 0.3920 0.5346 0.4523 0.9483 0.2529 0.6241 0.7761 0.5056 0.6369 0.6875 0.2601
0.5901 6.0 792 0.4903 0.3934 0.6037 0.4763 0.9493 0.3651 0.6230 0.6667 0.5775 0.6136 0.4848 0.3084
0.5901 7.0 924 0.6343 0.3991 0.5549 0.4643 0.9492 0.3286 0.6047 0.6301 0.5705 0.6216 0.5 0.2508
0.1282 8.0 1056 0.6686 0.4185 0.5691 0.4823 0.9507 0.3546 0.6565 0.7353 0.5786 0.6174 0.4615 0.2540
0.1282 9.0 1188 0.6801 0.4144 0.5610 0.4767 0.9507 0.3729 0.5616 0.7576 0.5569 0.5839 0.5263 0.2821
0.1282 10.0 1320 0.5843 0.4022 0.5976 0.4808 0.9493 0.3262 0.6667 0.6957 0.5417 0.6267 0.6286 0.2969
0.1282 11.0 1452 0.7041 0.4187 0.5813 0.4868 0.9508 0.3158 0.608 0.6857 0.5583 0.6174 0.7429 0.3145
0.0579 12.0 1584 0.7037 0.4143 0.5894 0.4866 0.9509 0.3310 0.6032 0.7164 0.5426 0.6497 0.6486 0.3207
0.0579 13.0 1716 0.6623 0.3961 0.5732 0.4684 0.9512 0.3490 0.6847 0.6462 0.5411 0.5987 0.6286 0.3038
0.0579 14.0 1848 0.7953 0.4443 0.5752 0.5013 0.9517 0.3704 0.6457 0.6667 0.5705 0.625 0.6667 0.3030
0.0579 15.0 1980 0.7321 0.4050 0.5976 0.4828 0.9482 0.3023 0.7156 0.7042 0.5815 0.6309 0.6667 0.2935
0.0281 16.0 2112 0.7175 0.4149 0.6240 0.4984 0.9503 0.3740 0.5816 0.6667 0.5756 0.6335 0.6667 0.3182
0.0281 17.0 2244 0.7463 0.4242 0.5915 0.4941 0.9507 0.3741 0.7130 0.7429 0.5678 0.6144 0.6486 0.2824
0.0281 18.0 2376 0.8040 0.4172 0.5732 0.4829 0.9531 0.3485 0.6838 0.7647 0.4946 0.6279 0.6286 0.3233
0.0156 19.0 2508 0.8922 0.4365 0.5589 0.4902 0.9525 0.4 0.6607 0.7500 0.5455 0.6013 0.6316 0.2803
0.0156 20.0 2640 0.8597 0.4072 0.5488 0.4675 0.9516 0.3333 0.6435 0.7213 0.5086 0.6536 0.5882 0.2975
0.0156 21.0 2772 0.7927 0.3986 0.5752 0.4709 0.9518 0.3759 0.6107 0.6857 0.5048 0.6582 0.7273 0.2802
0.0156 22.0 2904 0.9151 0.4234 0.5732 0.4870 0.9493 0.3590 0.7143 0.8060 0.5491 0.6174 0.7273 0.2305
0.0102 23.0 3036 0.9133 0.4231 0.5813 0.4897 0.9502 0.3185 0.6357 0.7941 0.55 0.6795 0.6061 0.2754
0.0102 24.0 3168 0.9799 0.4362 0.5285 0.4779 0.9515 0.3262 0.7339 0.7619 0.5193 0.6525 0.6452 0.2704

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1+cu102
  • Datasets 2.6.1
  • Tokenizers 0.13.1