File size: 2,314 Bytes
18f00fa cdd3c94 18f00fa cdd3c94 18f00fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
---
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
|