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Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5062
- Precision: 0.6603
- Recall: 0.4682
- F1: 0.5479
- Accuracy: 0.9355
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 107 | 0.3396 | 0.6470 | 0.4269 | 0.5144 | 0.9330 |
| No log | 2.0 | 214 | 0.3475 | 0.5948 | 0.4539 | 0.5149 | 0.9335 |
| No log | 3.0 | 321 | 0.3793 | 0.6613 | 0.4253 | 0.5177 | 0.9332 |
| No log | 4.0 | 428 | 0.3598 | 0.6195 | 0.4944 | 0.5500 | 0.9354 |
| 0.0409 | 5.0 | 535 | 0.3702 | 0.5802 | 0.4571 | 0.5113 | 0.9308 |
| 0.0409 | 6.0 | 642 | 0.4192 | 0.6546 | 0.4459 | 0.5305 | 0.9344 |
| 0.0409 | 7.0 | 749 | 0.4039 | 0.6360 | 0.4610 | 0.5346 | 0.9354 |
| 0.0409 | 8.0 | 856 | 0.4104 | 0.6564 | 0.4587 | 0.5400 | 0.9353 |
| 0.0409 | 9.0 | 963 | 0.3839 | 0.6283 | 0.4944 | 0.5534 | 0.9361 |
| 0.0132 | 10.0 | 1070 | 0.4331 | 0.6197 | 0.4547 | 0.5245 | 0.9339 |
| 0.0132 | 11.0 | 1177 | 0.4152 | 0.6196 | 0.4817 | 0.5420 | 0.9355 |
| 0.0132 | 12.0 | 1284 | 0.4654 | 0.6923 | 0.4507 | 0.5460 | 0.9353 |
| 0.0132 | 13.0 | 1391 | 0.4869 | 0.6739 | 0.4436 | 0.5350 | 0.9350 |
| 0.0132 | 14.0 | 1498 | 0.4297 | 0.6424 | 0.4769 | 0.5474 | 0.9353 |
| 0.0061 | 15.0 | 1605 | 0.4507 | 0.6272 | 0.4626 | 0.5325 | 0.9340 |
| 0.0061 | 16.0 | 1712 | 0.4410 | 0.6066 | 0.4793 | 0.5355 | 0.9335 |
| 0.0061 | 17.0 | 1819 | 0.4851 | 0.6639 | 0.4523 | 0.5381 | 0.9351 |
| 0.0061 | 18.0 | 1926 | 0.4815 | 0.6553 | 0.4563 | 0.5380 | 0.9346 |
| 0.0035 | 19.0 | 2033 | 0.5188 | 0.6780 | 0.4420 | 0.5351 | 0.9350 |
| 0.0035 | 20.0 | 2140 | 0.4986 | 0.6770 | 0.4698 | 0.5547 | 0.9363 |
| 0.0035 | 21.0 | 2247 | 0.4834 | 0.6552 | 0.4714 | 0.5483 | 0.9355 |
| 0.0035 | 22.0 | 2354 | 0.5094 | 0.6784 | 0.4595 | 0.5479 | 0.9358 |
| 0.0035 | 23.0 | 2461 | 0.4954 | 0.6583 | 0.4579 | 0.5401 | 0.9354 |
| 0.0026 | 24.0 | 2568 | 0.5035 | 0.6667 | 0.4595 | 0.5440 | 0.9354 |
| 0.0026 | 25.0 | 2675 | 0.5000 | 0.6599 | 0.4658 | 0.5461 | 0.9355 |
| 0.0026 | 26.0 | 2782 | 0.4968 | 0.6697 | 0.4738 | 0.5549 | 0.9357 |
| 0.0026 | 27.0 | 2889 | 0.4991 | 0.6545 | 0.4714 | 0.5481 | 0.9352 |
| 0.0026 | 28.0 | 2996 | 0.4936 | 0.6508 | 0.4769 | 0.5505 | 0.9353 |
| 0.0021 | 29.0 | 3103 | 0.5005 | 0.6535 | 0.4722 | 0.5482 | 0.9353 |
| 0.0021 | 30.0 | 3210 | 0.5062 | 0.6603 | 0.4682 | 0.5479 | 0.9355 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
