|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: PathologyBERT-meningioma |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# PathologyBERT-meningioma |
|
|
|
This model is a fine-tuned version of [tsantos/PathologyBERT](https://huggingface.co/tsantos/PathologyBERT) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.8123 |
|
- Accuracy: 0.8783 |
|
- Precision: 0.25 |
|
- Recall: 0.0833 |
|
- F1: 0.125 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 1e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 0 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 0.3723 | 1.0 | 71 | 0.5377 | 0.7652 | 0.0588 | 0.0833 | 0.0690 | |
|
| 0.3363 | 2.0 | 142 | 0.4191 | 0.8783 | 0.25 | 0.0833 | 0.125 | |
|
| 0.2773 | 3.0 | 213 | 0.4701 | 0.8870 | 0.3333 | 0.0833 | 0.1333 | |
|
| 0.2303 | 4.0 | 284 | 0.5831 | 0.8957 | 0.5 | 0.0833 | 0.1429 | |
|
| 0.1657 | 5.0 | 355 | 0.7083 | 0.8348 | 0.1111 | 0.0833 | 0.0952 | |
|
| 0.1228 | 6.0 | 426 | 1.0324 | 0.8 | 0.0769 | 0.0833 | 0.08 | |
|
| 0.0967 | 7.0 | 497 | 0.8103 | 0.8696 | 0.2 | 0.0833 | 0.1176 | |
|
| 0.0729 | 8.0 | 568 | 0.8711 | 0.8696 | 0.2 | 0.0833 | 0.1176 | |
|
| 0.0624 | 9.0 | 639 | 0.7968 | 0.8783 | 0.25 | 0.0833 | 0.125 | |
|
| 0.0534 | 10.0 | 710 | 0.8123 | 0.8783 | 0.25 | 0.0833 | 0.125 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.12.2 |
|
- Pytorch 1.10.1 |
|
- Datasets 1.15.0 |
|
- Tokenizers 0.10.3 |
|
|