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custom-ner-model
This model is a fine-tuned version of dccuchile/distilbert-base-spanish-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2706
- Precision: 0.8139
- Recall: 0.8624
- F1: 0.8374
- Accuracy: 0.9376
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 | 55 | 0.2457 | 0.7716 | 0.8211 | 0.7956 | 0.9346 |
| No log | 2.0 | 110 | 0.3008 | 0.75 | 0.8257 | 0.7860 | 0.9152 |
| No log | 3.0 | 165 | 0.2658 | 0.7712 | 0.8349 | 0.8018 | 0.9322 |
| No log | 4.0 | 220 | 0.2580 | 0.7583 | 0.8349 | 0.7948 | 0.9322 |
| No log | 5.0 | 275 | 0.2610 | 0.8079 | 0.8486 | 0.8277 | 0.9346 |
| No log | 6.0 | 330 | 0.2660 | 0.7699 | 0.8440 | 0.8053 | 0.9303 |
| No log | 7.0 | 385 | 0.2910 | 0.7830 | 0.8440 | 0.8124 | 0.9261 |
| No log | 8.0 | 440 | 0.2577 | 0.7913 | 0.8349 | 0.8125 | 0.9376 |
| No log | 9.0 | 495 | 0.2629 | 0.7948 | 0.8349 | 0.8143 | 0.9346 |
| 0.0437 | 10.0 | 550 | 0.2745 | 0.8070 | 0.8440 | 0.8251 | 0.9328 |
| 0.0437 | 11.0 | 605 | 0.2733 | 0.7860 | 0.8257 | 0.8054 | 0.9316 |
| 0.0437 | 12.0 | 660 | 0.2641 | 0.7965 | 0.8440 | 0.8196 | 0.9310 |
| 0.0437 | 13.0 | 715 | 0.2716 | 0.8139 | 0.8624 | 0.8374 | 0.9334 |
| 0.0437 | 14.0 | 770 | 0.2765 | 0.8210 | 0.8624 | 0.8412 | 0.9334 |
| 0.0437 | 15.0 | 825 | 0.2775 | 0.8253 | 0.8670 | 0.8456 | 0.9346 |
| 0.0437 | 16.0 | 880 | 0.2737 | 0.7897 | 0.8440 | 0.8160 | 0.9340 |
| 0.0437 | 17.0 | 935 | 0.2691 | 0.8043 | 0.8486 | 0.8259 | 0.9388 |
| 0.0437 | 18.0 | 990 | 0.2694 | 0.8246 | 0.8624 | 0.8430 | 0.9382 |
| 0.0157 | 19.0 | 1045 | 0.2732 | 0.8069 | 0.8624 | 0.8337 | 0.9346 |
| 0.0157 | 20.0 | 1100 | 0.2706 | 0.8139 | 0.8624 | 0.8374 | 0.9376 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
