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--- |
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base_model: smanjil/German-MedBERT |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: German-MedBERT |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# German-MedBERT |
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This model is a fine-tuned version of [smanjil/German-MedBERT](https://huggingface.co/smanjil/German-MedBERT) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5145 |
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- F1: 0.4561 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-07 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.693 | 1.0 | 189 | 0.6754 | 0.0698 | |
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| 0.6853 | 2.0 | 378 | 0.6626 | 0.0339 | |
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| 0.6654 | 3.0 | 567 | 0.6499 | 0.0488 | |
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| 0.6562 | 4.0 | 756 | 0.6399 | 0.0541 | |
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| 0.6554 | 5.0 | 945 | 0.6335 | 0.0556 | |
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| 0.6394 | 6.0 | 1134 | 0.6260 | 0.0571 | |
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| 0.6452 | 7.0 | 1323 | 0.6220 | 0.0571 | |
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| 0.6257 | 8.0 | 1512 | 0.6161 | 0.0571 | |
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| 0.6334 | 9.0 | 1701 | 0.6117 | 0.0571 | |
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| 0.6302 | 10.0 | 1890 | 0.6068 | 0.0571 | |
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| 0.6151 | 11.0 | 2079 | 0.6011 | 0.0571 | |
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| 0.6121 | 12.0 | 2268 | 0.5961 | 0.0571 | |
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| 0.6097 | 13.0 | 2457 | 0.5915 | 0.0571 | |
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| 0.5929 | 14.0 | 2646 | 0.5865 | 0.0556 | |
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| 0.5955 | 15.0 | 2835 | 0.5822 | 0.0556 | |
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| 0.5893 | 16.0 | 3024 | 0.5776 | 0.1053 | |
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| 0.5936 | 17.0 | 3213 | 0.5731 | 0.1 | |
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| 0.5769 | 18.0 | 3402 | 0.5687 | 0.1 | |
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| 0.5692 | 19.0 | 3591 | 0.5646 | 0.1 | |
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| 0.5739 | 20.0 | 3780 | 0.5604 | 0.2326 | |
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| 0.5705 | 21.0 | 3969 | 0.5564 | 0.2326 | |
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| 0.5651 | 22.0 | 4158 | 0.5525 | 0.2727 | |
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| 0.5654 | 23.0 | 4347 | 0.5494 | 0.2727 | |
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| 0.5527 | 24.0 | 4536 | 0.5456 | 0.2727 | |
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| 0.5542 | 25.0 | 4725 | 0.5425 | 0.2727 | |
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| 0.5464 | 26.0 | 4914 | 0.5395 | 0.2727 | |
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| 0.5383 | 27.0 | 5103 | 0.5364 | 0.3111 | |
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| 0.5323 | 28.0 | 5292 | 0.5348 | 0.3111 | |
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| 0.5343 | 29.0 | 5481 | 0.5318 | 0.3404 | |
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| 0.5305 | 30.0 | 5670 | 0.5299 | 0.4082 | |
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| 0.5252 | 31.0 | 5859 | 0.5278 | 0.4 | |
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| 0.516 | 32.0 | 6048 | 0.5270 | 0.3922 | |
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| 0.5181 | 33.0 | 6237 | 0.5243 | 0.4231 | |
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| 0.5202 | 34.0 | 6426 | 0.5230 | 0.4231 | |
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| 0.5068 | 35.0 | 6615 | 0.5224 | 0.4231 | |
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| 0.514 | 36.0 | 6804 | 0.5205 | 0.4528 | |
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| 0.5014 | 37.0 | 6993 | 0.5194 | 0.4528 | |
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| 0.4899 | 38.0 | 7182 | 0.5188 | 0.4444 | |
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| 0.5104 | 39.0 | 7371 | 0.5164 | 0.4364 | |
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| 0.4823 | 40.0 | 7560 | 0.5174 | 0.4444 | |
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| 0.515 | 41.0 | 7749 | 0.5155 | 0.4364 | |
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| 0.4906 | 42.0 | 7938 | 0.5154 | 0.4364 | |
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| 0.4853 | 43.0 | 8127 | 0.5158 | 0.4364 | |
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| 0.5006 | 44.0 | 8316 | 0.5153 | 0.4364 | |
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| 0.503 | 45.0 | 8505 | 0.5146 | 0.4561 | |
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| 0.4915 | 46.0 | 8694 | 0.5141 | 0.4561 | |
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| 0.4903 | 47.0 | 8883 | 0.5144 | 0.4561 | |
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| 0.4892 | 48.0 | 9072 | 0.5146 | 0.4561 | |
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| 0.4939 | 49.0 | 9261 | 0.5146 | 0.4561 | |
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| 0.5007 | 50.0 | 9450 | 0.5145 | 0.4561 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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