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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: BioELECTRA-LitCovid-v1.3.1 |
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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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# BioELECTRA-LitCovid-v1.3.1 |
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This model is a fine-tuned version of [kamalkraj/bioelectra-base-discriminator-pubmed](https://huggingface.co/kamalkraj/bioelectra-base-discriminator-pubmed) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6749 |
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- Hamming loss: 0.0257 |
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- F1 micro: 0.7955 |
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- F1 macro: 0.3005 |
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- F1 weighted: 0.8714 |
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- F1 samples: 0.8642 |
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- Precision micro: 0.6936 |
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- Precision macro: 0.2470 |
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- Precision weighted: 0.8294 |
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- Precision samples: 0.8463 |
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- Recall micro: 0.9326 |
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- Recall macro: 0.7358 |
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- Recall weighted: 0.9326 |
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- Recall samples: 0.9427 |
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- Roc Auc: 0.9546 |
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- Accuracy: 0.6664 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Hamming loss | F1 micro | F1 macro | F1 weighted | F1 samples | Precision micro | Precision macro | Precision weighted | Precision samples | Recall micro | Recall macro | Recall weighted | Recall samples | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| |
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| 1.385 | 1.0 | 2272 | 0.6961 | 0.0592 | 0.6188 | 0.2176 | 0.7539 | 0.7422 | 0.4725 | 0.1706 | 0.6672 | 0.6890 | 0.8965 | 0.6896 | 0.8965 | 0.9065 | 0.9199 | 0.3887 | |
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| 1.2034 | 2.0 | 4544 | 0.6242 | 0.0342 | 0.7421 | 0.2668 | 0.8404 | 0.8354 | 0.6231 | 0.2180 | 0.7922 | 0.8120 | 0.9172 | 0.6872 | 0.9172 | 0.9319 | 0.9429 | 0.5906 | |
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| 1.0857 | 3.0 | 6816 | 0.6185 | 0.0270 | 0.7869 | 0.2949 | 0.8615 | 0.8587 | 0.6815 | 0.2402 | 0.8153 | 0.8382 | 0.9308 | 0.7164 | 0.9308 | 0.9437 | 0.9531 | 0.6444 | |
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| 0.8846 | 4.0 | 9088 | 0.6143 | 0.0260 | 0.7936 | 0.2994 | 0.8677 | 0.8626 | 0.6916 | 0.2460 | 0.8237 | 0.8444 | 0.9309 | 0.7254 | 0.9309 | 0.9421 | 0.9537 | 0.6594 | |
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| 0.6753 | 5.0 | 11360 | 0.6749 | 0.0257 | 0.7955 | 0.3005 | 0.8714 | 0.8642 | 0.6936 | 0.2470 | 0.8294 | 0.8463 | 0.9326 | 0.7358 | 0.9326 | 0.9427 | 0.9546 | 0.6664 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.13.3 |
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