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distilbert-base-uncased-finetuned-ner_0212
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1755
- Precision: 0.8894
- Recall: 0.9402
- F1: 0.9141
- Accuracy: 0.9561
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 105 | 0.4362 | 0.6638 | 0.7654 | 0.7110 | 0.8662 |
| No log | 2.0 | 210 | 0.2527 | 0.7860 | 0.8686 | 0.8252 | 0.9127 |
| No log | 3.0 | 315 | 0.2411 | 0.8339 | 0.8942 | 0.8630 | 0.9223 |
| No log | 4.0 | 420 | 0.1801 | 0.8444 | 0.9021 | 0.8723 | 0.9403 |
| 0.3472 | 5.0 | 525 | 0.1569 | 0.8470 | 0.9166 | 0.8804 | 0.9439 |
| 0.3472 | 6.0 | 630 | 0.1424 | 0.8643 | 0.9205 | 0.8915 | 0.9531 |
| 0.3472 | 7.0 | 735 | 0.1526 | 0.8764 | 0.9271 | 0.9010 | 0.9476 |
| 0.3472 | 8.0 | 840 | 0.1804 | 0.8826 | 0.9284 | 0.9049 | 0.9452 |
| 0.3472 | 9.0 | 945 | 0.1650 | 0.8809 | 0.9330 | 0.9062 | 0.9504 |
| 0.068 | 10.0 | 1050 | 0.1458 | 0.8819 | 0.9271 | 0.9039 | 0.9574 |
| 0.068 | 11.0 | 1155 | 0.1618 | 0.8810 | 0.9336 | 0.9065 | 0.9511 |
| 0.068 | 12.0 | 1260 | 0.1817 | 0.8865 | 0.9343 | 0.9098 | 0.9489 |
| 0.068 | 13.0 | 1365 | 0.1530 | 0.8890 | 0.9363 | 0.912 | 0.9572 |
| 0.068 | 14.0 | 1470 | 0.1643 | 0.9032 | 0.9382 | 0.9204 | 0.9552 |
| 0.0342 | 15.0 | 1575 | 0.1710 | 0.9016 | 0.9336 | 0.9174 | 0.9550 |
| 0.0342 | 16.0 | 1680 | 0.1736 | 0.8879 | 0.9369 | 0.9118 | 0.9554 |
| 0.0342 | 17.0 | 1785 | 0.1722 | 0.8903 | 0.9382 | 0.9136 | 0.9556 |
| 0.0342 | 18.0 | 1890 | 0.1713 | 0.8848 | 0.9389 | 0.9111 | 0.9525 |
| 0.0342 | 19.0 | 1995 | 0.1692 | 0.88 | 0.9396 | 0.9088 | 0.9573 |
| 0.0224 | 20.0 | 2100 | 0.1755 | 0.8894 | 0.9402 | 0.9141 | 0.9561 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.13.0+cu117
- Datasets 2.8.0
- Tokenizers 0.12.1
