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---
license: apache-2.0
base_model: GerMedBERT/medbert-512
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: GerMedBert_NORMTOP50_V02_BRONCO
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GerMedBert_NORMTOP50_V02_BRONCO
This model is a fine-tuned version of [GerMedBERT/medbert-512](https://huggingface.co./GerMedBERT/medbert-512) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0166
- F1 Score: 0.8624
- Precision: 0.8939
- Recall: 0.8329
- Accuracy: 0.8594
- Num Input Tokens Seen: 15165836
## 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 | F1 Score | Precision | Recall | Accuracy | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:-----------------:|
| No log | 0.25 | 81 | 0.1649 | 0.0 | 1.0 | 0.0 | 0.5920 | 190512 |
| 0.2195 | 0.5 | 162 | 0.0988 | 0.0 | 1.0 | 0.0 | 0.5920 | 380384 |
| 0.2195 | 0.75 | 243 | 0.0825 | 0.0 | 1.0 | 0.0 | 0.5920 | 570256 |
| 0.0855 | 1.0 | 324 | 0.0768 | 0.0 | 1.0 | 0.0 | 0.5920 | 759687 |
| 0.0855 | 1.25 | 405 | 0.0744 | 0.0 | 1.0 | 0.0 | 0.5920 | 948599 |
| 0.0737 | 1.5 | 486 | 0.0688 | 0.0 | 1.0 | 0.0 | 0.5920 | 1138791 |
| 0.0737 | 1.75 | 567 | 0.0652 | 0.1979 | 0.94 | 0.1106 | 0.6059 | 1327703 |
| 0.0666 | 2.0 | 648 | 0.0600 | 0.0982 | 0.9565 | 0.0518 | 0.5972 | 1516814 |
| 0.0666 | 2.25 | 729 | 0.0553 | 0.1231 | 0.9333 | 0.0659 | 0.5972 | 1706686 |
| 0.0572 | 2.5 | 810 | 0.0521 | 0.4093 | 0.8394 | 0.2706 | 0.6458 | 1896878 |
| 0.0572 | 2.75 | 891 | 0.0490 | 0.4326 | 0.8777 | 0.2871 | 0.6510 | 2086110 |
| 0.0499 | 3.0 | 972 | 0.0456 | 0.2897 | 1.0 | 0.1694 | 0.6476 | 2275221 |
| 0.0499 | 3.25 | 1053 | 0.0423 | 0.5060 | 0.925 | 0.3482 | 0.6667 | 2465413 |
| 0.0441 | 3.5 | 1134 | 0.0406 | 0.5386 | 0.8913 | 0.3859 | 0.6736 | 2654965 |
| 0.0441 | 3.75 | 1215 | 0.0396 | 0.5979 | 0.8419 | 0.4635 | 0.6858 | 2845157 |
| 0.0393 | 4.0 | 1296 | 0.0377 | 0.6517 | 0.9004 | 0.5106 | 0.7083 | 3034556 |
| 0.0393 | 4.25 | 1377 | 0.0357 | 0.6319 | 0.9075 | 0.4847 | 0.6997 | 3224428 |
| 0.0361 | 4.5 | 1458 | 0.0346 | 0.6154 | 0.9245 | 0.4612 | 0.7066 | 3414620 |
| 0.0361 | 4.75 | 1539 | 0.0334 | 0.6258 | 0.8987 | 0.48 | 0.7101 | 3604172 |
| 0.032 | 5.0 | 1620 | 0.0321 | 0.6775 | 0.9124 | 0.5388 | 0.7292 | 3793603 |
| 0.032 | 5.25 | 1701 | 0.0306 | 0.7081 | 0.9176 | 0.5765 | 0.7378 | 3983155 |
| 0.0293 | 5.5 | 1782 | 0.0302 | 0.6928 | 0.9019 | 0.5624 | 0.7361 | 4172387 |
| 0.0293 | 5.75 | 1863 | 0.0292 | 0.6657 | 0.9247 | 0.52 | 0.7240 | 4362579 |
| 0.0273 | 6.0 | 1944 | 0.0287 | 0.7365 | 0.9253 | 0.6118 | 0.7691 | 4552330 |
| 0.0273 | 6.25 | 2025 | 0.0275 | 0.7215 | 0.9328 | 0.5882 | 0.7552 | 4741882 |
| 0.0258 | 6.5 | 2106 | 0.0272 | 0.7275 | 0.9024 | 0.6094 | 0.7517 | 4930794 |
| 0.0258 | 6.75 | 2187 | 0.0260 | 0.7451 | 0.9204 | 0.6259 | 0.7726 | 5120026 |
| 0.0228 | 7.0 | 2268 | 0.0260 | 0.7247 | 0.9203 | 0.5976 | 0.7656 | 5309137 |
| 0.0228 | 7.25 | 2349 | 0.0249 | 0.7867 | 0.9077 | 0.6941 | 0.7969 | 5499649 |
| 0.0218 | 7.5 | 2430 | 0.0246 | 0.7572 | 0.9079 | 0.6494 | 0.7778 | 5688881 |
| 0.0218 | 7.75 | 2511 | 0.0239 | 0.7779 | 0.9088 | 0.68 | 0.7882 | 5878113 |
| 0.02 | 8.0 | 2592 | 0.0239 | 0.7835 | 0.8994 | 0.6941 | 0.7899 | 6067224 |
| 0.02 | 8.25 | 2673 | 0.0229 | 0.7711 | 0.9159 | 0.6659 | 0.7917 | 6256456 |
| 0.0184 | 8.5 | 2754 | 0.0227 | 0.7705 | 0.8969 | 0.6753 | 0.7917 | 6446328 |
| 0.0184 | 8.75 | 2835 | 0.0226 | 0.7782 | 0.8671 | 0.7059 | 0.7899 | 6636520 |
| 0.0182 | 9.0 | 2916 | 0.0224 | 0.7937 | 0.8988 | 0.7106 | 0.8003 | 6825951 |
| 0.0182 | 9.25 | 2997 | 0.0217 | 0.7815 | 0.8939 | 0.6941 | 0.7951 | 7015183 |
| 0.0172 | 9.5 | 3078 | 0.0213 | 0.8156 | 0.9101 | 0.7388 | 0.8212 | 7205375 |
| 0.0172 | 9.75 | 3159 | 0.0211 | 0.8063 | 0.9086 | 0.7247 | 0.8142 | 7394927 |
| 0.0154 | 10.0 | 3240 | 0.0216 | 0.8246 | 0.8820 | 0.7741 | 0.8212 | 7583366 |
| 0.0154 | 10.25 | 3321 | 0.0204 | 0.7831 | 0.8943 | 0.6965 | 0.8021 | 7772598 |
| 0.0145 | 10.5 | 3402 | 0.0201 | 0.8185 | 0.9034 | 0.7482 | 0.8229 | 7962470 |
| 0.0145 | 10.75 | 3483 | 0.0200 | 0.8261 | 0.9048 | 0.76 | 0.8264 | 8152662 |
| 0.0143 | 11.0 | 3564 | 0.0198 | 0.8238 | 0.8929 | 0.7647 | 0.8281 | 8341773 |
| 0.0143 | 11.25 | 3645 | 0.0196 | 0.8229 | 0.8972 | 0.76 | 0.8264 | 8531645 |
| 0.0131 | 11.5 | 3726 | 0.0193 | 0.8231 | 0.8817 | 0.7718 | 0.8212 | 8720877 |
| 0.0131 | 11.75 | 3807 | 0.0195 | 0.8152 | 0.8822 | 0.7576 | 0.8177 | 8910109 |
| 0.0129 | 12.0 | 3888 | 0.0192 | 0.8263 | 0.9119 | 0.7553 | 0.8299 | 9099860 |
| 0.0129 | 12.25 | 3969 | 0.0188 | 0.8229 | 0.8972 | 0.76 | 0.8212 | 9289412 |
| 0.0116 | 12.5 | 4050 | 0.0191 | 0.8123 | 0.8883 | 0.7482 | 0.8247 | 9479284 |
| 0.0116 | 12.75 | 4131 | 0.0181 | 0.8417 | 0.9030 | 0.7882 | 0.8472 | 9669156 |
| 0.0115 | 13.0 | 4212 | 0.0180 | 0.8398 | 0.8895 | 0.7953 | 0.8420 | 9857947 |
| 0.0115 | 13.25 | 4293 | 0.0177 | 0.8445 | 0.9126 | 0.7859 | 0.8455 | 10045899 |
| 0.0108 | 13.5 | 4374 | 0.0179 | 0.8426 | 0.8901 | 0.8 | 0.8438 | 10236091 |
| 0.0108 | 13.75 | 4455 | 0.0179 | 0.8519 | 0.8961 | 0.8118 | 0.8524 | 10426283 |
| 0.0103 | 14.0 | 4536 | 0.0177 | 0.8392 | 0.9003 | 0.7859 | 0.8420 | 10615394 |
| 0.0103 | 14.25 | 4617 | 0.0176 | 0.8603 | 0.9062 | 0.8188 | 0.8594 | 10805906 |
| 0.0097 | 14.5 | 4698 | 0.0173 | 0.8475 | 0.904 | 0.7976 | 0.8507 | 10995458 |
| 0.0097 | 14.75 | 4779 | 0.0175 | 0.8511 | 0.9003 | 0.8071 | 0.8524 | 11185650 |
| 0.0095 | 15.0 | 4860 | 0.0173 | 0.8501 | 0.8979 | 0.8071 | 0.8490 | 11375081 |
| 0.0095 | 15.25 | 4941 | 0.0175 | 0.8451 | 0.8927 | 0.8024 | 0.8472 | 11564633 |
| 0.009 | 15.5 | 5022 | 0.0174 | 0.8483 | 0.8912 | 0.8094 | 0.8490 | 11754185 |
| 0.009 | 15.75 | 5103 | 0.0172 | 0.8490 | 0.8956 | 0.8071 | 0.8438 | 11943417 |
| 0.009 | 16.0 | 5184 | 0.0174 | 0.8547 | 0.8883 | 0.8235 | 0.8490 | 12132528 |
| 0.009 | 16.25 | 5265 | 0.0171 | 0.8519 | 0.8961 | 0.8118 | 0.8490 | 12322400 |
| 0.0085 | 16.5 | 5346 | 0.0169 | 0.8537 | 0.8861 | 0.8235 | 0.8524 | 12511632 |
| 0.0085 | 16.75 | 5427 | 0.0169 | 0.8498 | 0.8915 | 0.8118 | 0.8524 | 12701184 |
| 0.0084 | 17.0 | 5508 | 0.0169 | 0.8487 | 0.8892 | 0.8118 | 0.8507 | 12889655 |
| 0.0084 | 17.25 | 5589 | 0.0167 | 0.8634 | 0.8962 | 0.8329 | 0.8524 | 13079527 |
| 0.0079 | 17.5 | 5670 | 0.0168 | 0.8504 | 0.8958 | 0.8094 | 0.8472 | 13269399 |
| 0.0079 | 17.75 | 5751 | 0.0168 | 0.8606 | 0.8957 | 0.8282 | 0.8594 | 13458631 |
| 0.0081 | 18.0 | 5832 | 0.0167 | 0.8564 | 0.8949 | 0.8212 | 0.8542 | 13648382 |
| 0.0081 | 18.25 | 5913 | 0.0166 | 0.8620 | 0.8959 | 0.8306 | 0.8576 | 13838254 |
| 0.0078 | 18.5 | 5994 | 0.0166 | 0.8533 | 0.8964 | 0.8141 | 0.8542 | 14028126 |
| 0.0078 | 18.75 | 6075 | 0.0167 | 0.8610 | 0.8937 | 0.8306 | 0.8611 | 14217678 |
| 0.0078 | 19.0 | 6156 | 0.0166 | 0.8610 | 0.8937 | 0.8306 | 0.8594 | 14407109 |
| 0.0078 | 19.25 | 6237 | 0.0166 | 0.8637 | 0.8942 | 0.8353 | 0.8594 | 14596341 |
| 0.0072 | 19.5 | 6318 | 0.0166 | 0.8620 | 0.8959 | 0.8306 | 0.8594 | 14786213 |
| 0.0072 | 19.75 | 6399 | 0.0166 | 0.8624 | 0.8939 | 0.8329 | 0.8594 | 14976085 |
| 0.008 | 20.0 | 6480 | 0.0166 | 0.8624 | 0.8939 | 0.8329 | 0.8594 | 15165836 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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