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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: PathologyBERT-meningioma |
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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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# PathologyBERT-meningioma |
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This model is a fine-tuned version of [tsantos/PathologyBERT](https://huggingface.co/tsantos/PathologyBERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8123 |
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- Accuracy: 0.8783 |
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- Precision: 0.25 |
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- Recall: 0.0833 |
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- F1: 0.125 |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 0 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.3723 | 1.0 | 71 | 0.5377 | 0.7652 | 0.0588 | 0.0833 | 0.0690 | |
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| 0.3363 | 2.0 | 142 | 0.4191 | 0.8783 | 0.25 | 0.0833 | 0.125 | |
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| 0.2773 | 3.0 | 213 | 0.4701 | 0.8870 | 0.3333 | 0.0833 | 0.1333 | |
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| 0.2303 | 4.0 | 284 | 0.5831 | 0.8957 | 0.5 | 0.0833 | 0.1429 | |
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| 0.1657 | 5.0 | 355 | 0.7083 | 0.8348 | 0.1111 | 0.0833 | 0.0952 | |
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| 0.1228 | 6.0 | 426 | 1.0324 | 0.8 | 0.0769 | 0.0833 | 0.08 | |
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| 0.0967 | 7.0 | 497 | 0.8103 | 0.8696 | 0.2 | 0.0833 | 0.1176 | |
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| 0.0729 | 8.0 | 568 | 0.8711 | 0.8696 | 0.2 | 0.0833 | 0.1176 | |
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| 0.0624 | 9.0 | 639 | 0.7968 | 0.8783 | 0.25 | 0.0833 | 0.125 | |
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| 0.0534 | 10.0 | 710 | 0.8123 | 0.8783 | 0.25 | 0.0833 | 0.125 | |
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### Framework versions |
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- Transformers 4.12.2 |
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- Pytorch 1.10.1 |
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- Datasets 1.15.0 |
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- Tokenizers 0.10.3 |
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