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ner-distillbert-ner
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: 0.1179
- Precision: 0.8602
- Recall: 0.8497
- F1: 0.8549
- Accuracy: 0.9707
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: 0.0001
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 13 | 0.3151 | 0.3193 | 0.2684 | 0.2917 | 0.8755 |
| No log | 2.0 | 26 | 0.1966 | 0.6320 | 0.4663 | 0.5366 | 0.9379 |
| No log | 3.0 | 39 | 0.1332 | 0.7932 | 0.7469 | 0.7694 | 0.9608 |
| No log | 4.0 | 52 | 0.1173 | 0.8077 | 0.8313 | 0.8193 | 0.9652 |
| No log | 5.0 | 65 | 0.1093 | 0.8530 | 0.8190 | 0.8357 | 0.9685 |
| No log | 6.0 | 78 | 0.1123 | 0.8383 | 0.8589 | 0.8485 | 0.9676 |
| No log | 7.0 | 91 | 0.1203 | 0.8501 | 0.8436 | 0.8468 | 0.9669 |
| No log | 8.0 | 104 | 0.1165 | 0.8628 | 0.8390 | 0.8507 | 0.9697 |
| No log | 9.0 | 117 | 0.1168 | 0.8585 | 0.8466 | 0.8525 | 0.9701 |
| No log | 10.0 | 130 | 0.1179 | 0.8602 | 0.8497 | 0.8549 | 0.9707 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
