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bert-base-german-cased-20000-ner-uncased
This model is a fine-tuned version of dbmdz/bert-base-german-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0617
- Precision: 0.8871
- Recall: 0.9013
- F1: 0.8941
- Accuracy: 0.9848
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: 5e-05
- train_batch_size: 96
- eval_batch_size: 96
- 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 | 0.34 | 64 | 0.0573 | 0.8859 | 0.8526 | 0.8689 | 0.9837 |
| No log | 0.68 | 128 | 0.0654 | 0.8107 | 0.8957 | 0.8511 | 0.9808 |
| No log | 1.02 | 192 | 0.0531 | 0.8654 | 0.8846 | 0.8749 | 0.9842 |
| No log | 1.35 | 256 | 0.0467 | 0.8847 | 0.8853 | 0.8850 | 0.9857 |
| No log | 1.69 | 320 | 0.0466 | 0.9102 | 0.8883 | 0.8992 | 0.9864 |
| No log | 2.03 | 384 | 0.0467 | 0.8794 | 0.8951 | 0.8872 | 0.9854 |
| No log | 2.37 | 448 | 0.0520 | 0.8864 | 0.9001 | 0.8932 | 0.9851 |
| 0.0531 | 2.71 | 512 | 0.0549 | 0.8894 | 0.8877 | 0.8885 | 0.9854 |
| 0.0531 | 3.05 | 576 | 0.0534 | 0.8942 | 0.8920 | 0.8931 | 0.9857 |
| 0.0531 | 3.39 | 640 | 0.0526 | 0.8917 | 0.8994 | 0.8956 | 0.9856 |
| 0.0531 | 3.72 | 704 | 0.0576 | 0.9049 | 0.8976 | 0.9012 | 0.9857 |
| 0.0531 | 4.06 | 768 | 0.0700 | 0.8529 | 0.9229 | 0.8865 | 0.9830 |
| 0.0531 | 4.4 | 832 | 0.0657 | 0.8716 | 0.9167 | 0.8936 | 0.9840 |
| 0.0531 | 4.74 | 896 | 0.0617 | 0.8871 | 0.9013 | 0.8941 | 0.9848 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
