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--- |
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license: apache-2.0 |
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base_model: GerMedBERT/medbert-512 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: GerMedBert_ATTR_V02_BRONCO |
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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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# GerMedBert_ATTR_V02_BRONCO |
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This model is a fine-tuned version of [GerMedBERT/medbert-512](https://huggingface.co./GerMedBERT/medbert-512) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0593 |
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- F1 Score: 0.8187 |
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- Precision: 0.8235 |
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- Recall: 0.8140 |
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- Accuracy: 0.8993 |
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- Num Input Tokens Seen: 4204246 |
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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-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Input Tokens Seen | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:-----------------:| |
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| No log | 0.25 | 81 | 0.1949 | 0.0 | 1.0 | 0.0 | 0.7240 | 175856 | |
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| 0.206 | 0.5 | 162 | 0.1196 | 0.4123 | 0.8393 | 0.2733 | 0.7847 | 349280 | |
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| 0.206 | 0.75 | 243 | 0.0945 | 0.6296 | 0.8673 | 0.4942 | 0.8351 | 526096 | |
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| 0.1019 | 1.0 | 324 | 0.0792 | 0.7584 | 0.8 | 0.7209 | 0.8698 | 702407 | |
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| 0.1019 | 1.25 | 405 | 0.0697 | 0.7874 | 0.7784 | 0.7965 | 0.8785 | 877047 | |
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| 0.0715 | 1.5 | 486 | 0.0697 | 0.7778 | 0.7447 | 0.8140 | 0.8715 | 1052647 | |
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| 0.0715 | 1.75 | 567 | 0.0673 | 0.7568 | 0.7826 | 0.7326 | 0.8715 | 1227287 | |
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| 0.0638 | 2.0 | 648 | 0.0680 | 0.7781 | 0.7358 | 0.8256 | 0.8715 | 1400330 | |
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| 0.0638 | 2.25 | 729 | 0.0622 | 0.7965 | 0.8084 | 0.7849 | 0.8941 | 1577786 | |
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| 0.0445 | 2.5 | 810 | 0.0593 | 0.8012 | 0.7943 | 0.8081 | 0.8906 | 1751722 | |
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| 0.0445 | 2.75 | 891 | 0.0583 | 0.8023 | 0.8023 | 0.8023 | 0.8906 | 1927258 | |
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| 0.0396 | 3.0 | 972 | 0.0579 | 0.8161 | 0.8068 | 0.8256 | 0.8993 | 2100749 | |
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| 0.0396 | 3.25 | 1053 | 0.0598 | 0.8125 | 0.7944 | 0.8314 | 0.8941 | 2276989 | |
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| 0.0289 | 3.5 | 1134 | 0.0592 | 0.8036 | 0.8232 | 0.7849 | 0.8941 | 2451501 | |
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| 0.0289 | 3.75 | 1215 | 0.0585 | 0.7954 | 0.7886 | 0.8023 | 0.8906 | 2628573 | |
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| 0.0271 | 4.0 | 1296 | 0.0571 | 0.8171 | 0.8034 | 0.8314 | 0.8993 | 2802576 | |
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| 0.0271 | 4.25 | 1377 | 0.0581 | 0.8235 | 0.8333 | 0.8140 | 0.9045 | 2978368 | |
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| 0.0194 | 4.5 | 1458 | 0.0619 | 0.7978 | 0.7717 | 0.8256 | 0.8837 | 3154544 | |
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| 0.0194 | 4.75 | 1539 | 0.0612 | 0.8048 | 0.8323 | 0.7791 | 0.8958 | 3330400 | |
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| 0.0193 | 5.0 | 1620 | 0.0585 | 0.8059 | 0.8155 | 0.7965 | 0.8958 | 3505555 | |
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| 0.0193 | 5.25 | 1701 | 0.0587 | 0.8187 | 0.8235 | 0.8140 | 0.9010 | 3680771 | |
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| 0.0153 | 5.5 | 1782 | 0.0592 | 0.8242 | 0.8171 | 0.8314 | 0.9010 | 3856947 | |
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| 0.0153 | 5.75 | 1863 | 0.0592 | 0.8163 | 0.8187 | 0.8140 | 0.8993 | 4030371 | |
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| 0.0146 | 6.0 | 1944 | 0.0593 | 0.8187 | 0.8235 | 0.8140 | 0.8993 | 4204246 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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