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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: libCap_prBERTbfd_clf |
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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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# libCap_prBERTbfd_clf |
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This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5197 |
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- Accuracy: 0.7457 |
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- F1: 0.7940 |
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- Precision: 0.7567 |
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- Recall: 0.8352 |
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- Auroc: 0.7268 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 4096 |
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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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Auroc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| |
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| No log | 0.98 | 34 | 0.6393 | 0.6396 | 0.7053 | 0.6782 | 0.7345 | 0.6197 | |
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| No log | 1.98 | 68 | 0.5713 | 0.6962 | 0.7499 | 0.7256 | 0.7759 | 0.6795 | |
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| No log | 2.98 | 102 | 0.5652 | 0.7126 | 0.7388 | 0.7918 | 0.6924 | 0.7168 | |
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| No log | 3.98 | 136 | 0.5360 | 0.7330 | 0.7896 | 0.7345 | 0.8536 | 0.7076 | |
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| No log | 4.98 | 170 | 0.5223 | 0.7423 | 0.7830 | 0.7740 | 0.7921 | 0.7318 | |
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| No log | 5.98 | 204 | 0.5180 | 0.7454 | 0.7882 | 0.7699 | 0.8075 | 0.7323 | |
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| No log | 6.98 | 238 | 0.5179 | 0.7440 | 0.7934 | 0.7537 | 0.8376 | 0.7243 | |
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| No log | 7.98 | 272 | 0.5197 | 0.7457 | 0.7940 | 0.7567 | 0.8352 | 0.7268 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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