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