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distilbert-base-uncased-finetuned-ner

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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-uncased-finetuned-ner
    results: []

distilbert-base-uncased-finetuned-ner

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

  • Loss: 0.4331
  • Precision: 0.7801
  • Recall: 0.7129
  • F1: 0.745
  • Accuracy: 0.9363

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
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 23 0.8283 0.0 0.0 0.0 0.8332
No log 2.0 46 0.5781 0.4474 0.0813 0.1377 0.8482
No log 3.0 69 0.4927 0.4841 0.3636 0.4153 0.8913
No log 4.0 92 0.4348 0.4657 0.4545 0.4600 0.9044
No log 5.0 115 0.4293 0.5561 0.4976 0.5253 0.9088
No log 6.0 138 0.3934 0.6313 0.5981 0.6143 0.9244
No log 7.0 161 0.3961 0.7219 0.6459 0.6818 0.9313
No log 8.0 184 0.3648 0.7098 0.6555 0.6816 0.9325
No log 9.0 207 0.3961 0.7582 0.6603 0.7059 0.9357
No log 10.0 230 0.3800 0.7474 0.6794 0.7118 0.9350
No log 11.0 253 0.3661 0.7474 0.6794 0.7118 0.9332
No log 12.0 276 0.3697 0.7619 0.6890 0.7236 0.9344
No log 13.0 299 0.3829 0.7660 0.6890 0.7254 0.9350
No log 14.0 322 0.3859 0.7849 0.6986 0.7392 0.9350
No log 15.0 345 0.3760 0.7946 0.7033 0.7462 0.9375
No log 16.0 368 0.3609 0.7602 0.7129 0.7358 0.9357
No log 17.0 391 0.3687 0.7766 0.6986 0.7355 0.9350
No log 18.0 414 0.3856 0.8043 0.7081 0.7532 0.9375
No log 19.0 437 0.3901 0.7861 0.7033 0.7424 0.9369
No log 20.0 460 0.4151 0.8276 0.6890 0.7520 0.9388
No log 21.0 483 0.3892 0.7824 0.7225 0.7512 0.9382
0.1775 22.0 506 0.3952 0.7947 0.7225 0.7569 0.9375
0.1775 23.0 529 0.3906 0.7817 0.7368 0.7586 0.9382
0.1775 24.0 552 0.4132 0.8156 0.6986 0.7526 0.9413
0.1775 25.0 575 0.4048 0.7979 0.7177 0.7557 0.9388
0.1775 26.0 598 0.4026 0.7772 0.7177 0.7463 0.9363
0.1775 27.0 621 0.4084 0.7789 0.7081 0.7419 0.9363
0.1775 28.0 644 0.4081 0.7865 0.7225 0.7531 0.9375
0.1775 29.0 667 0.4058 0.7795 0.7273 0.7525 0.9375
0.1775 30.0 690 0.4100 0.7772 0.7177 0.7463 0.9369
0.1775 31.0 713 0.4146 0.7824 0.7225 0.7512 0.9363
0.1775 32.0 736 0.4142 0.7865 0.7225 0.7531 0.9363
0.1775 33.0 759 0.4168 0.7824 0.7225 0.7512 0.9369
0.1775 34.0 782 0.4367 0.8122 0.7033 0.7538 0.9388
0.1775 35.0 805 0.4282 0.7884 0.7129 0.7487 0.9363
0.1775 36.0 828 0.4249 0.7842 0.7129 0.7469 0.9357
0.1775 37.0 851 0.4297 0.7884 0.7129 0.7487 0.9363
0.1775 38.0 874 0.4218 0.7824 0.7225 0.7512 0.9375
0.1775 39.0 897 0.4267 0.7842 0.7129 0.7469 0.9363
0.1775 40.0 920 0.4272 0.7937 0.7177 0.7538 0.9369
0.1775 41.0 943 0.4308 0.7926 0.7129 0.7506 0.9363
0.1775 42.0 966 0.4390 0.7884 0.7129 0.7487 0.9363
0.1775 43.0 989 0.4366 0.7914 0.7081 0.7475 0.9375
0.0065 44.0 1012 0.4311 0.7749 0.7081 0.74 0.9350
0.0065 45.0 1035 0.4276 0.7760 0.7129 0.7431 0.9357
0.0065 46.0 1058 0.4313 0.7801 0.7129 0.745 0.9357
0.0065 47.0 1081 0.4330 0.7801 0.7129 0.745 0.9357
0.0065 48.0 1104 0.4325 0.7801 0.7129 0.745 0.9363
0.0065 49.0 1127 0.4328 0.7801 0.7129 0.745 0.9363
0.0065 50.0 1150 0.4331 0.7801 0.7129 0.745 0.9363

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.0
  • Tokenizers 0.15.0