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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- marker-associations-binary-base |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: marker-associations-binary-base |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: marker-associations-binary-base |
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type: marker-associations-binary-base |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7981651376146789 |
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- name: Recall |
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type: recall |
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value: 0.9560439560439561 |
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- name: F1 |
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type: f1 |
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value: 0.87 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8884120171673819 |
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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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# marker-associations-binary-base |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the marker-associations-binary-base dataset. |
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It achieves the following results on the evaluation set: |
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### Gene Results |
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- Precision = 0.808 |
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- Recall = 0.940 |
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- F1 = 0.869 |
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- Accuracy = 0.862 |
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- AUC = 0.944 |
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### Chemical Results |
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- Precision = 0.774 |
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- Recall = 1.0 |
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- F1 = 0.873 |
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- Accuracy = 0.926 |
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- AUC = 0.964 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 1 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:| |
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| No log | 1.0 | 88 | 0.3266 | 0.8191 | 0.8462 | 0.8324 | 0.8670 | 0.9313 | |
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| No log | 2.0 | 176 | 0.3335 | 0.7870 | 0.9341 | 0.8543 | 0.8755 | 0.9465 | |
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| No log | 3.0 | 264 | 0.4243 | 0.7982 | 0.9560 | 0.87 | 0.8884 | 0.9516 | |
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| No log | 4.0 | 352 | 0.5388 | 0.825 | 0.7253 | 0.7719 | 0.8326 | 0.9384 | |
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| No log | 5.0 | 440 | 0.7101 | 0.8537 | 0.7692 | 0.8092 | 0.8584 | 0.9416 | |
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| 0.1824 | 6.0 | 528 | 0.6175 | 0.8242 | 0.8242 | 0.8242 | 0.8627 | 0.9478 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.0+cu111 |
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- Tokenizers 0.10.3 |
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