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---
base_model: dmis-lab/biobert-base-cased-v1.2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cause-biobert-biocause
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. -->
# cause-biobert-biocause
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5157
- Precision: 0.2230
- Recall: 0.4277
- F1: 0.2931
- Accuracy: 0.8241
- Cause P: 0.2230
- Cause R: 0.4277
- Cause F1: 0.2931
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Cause P | Cause R | Cause F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:-------:|:--------:|
| 0.6993 | 0.25 | 20 | 0.6314 | 0.0556 | 0.1698 | 0.0837 | 0.7587 | 0.0556 | 0.1698 | 0.0837 |
| 0.6993 | 0.5 | 40 | 0.5747 | 0.0826 | 0.2327 | 0.1219 | 0.6524 | 0.0826 | 0.2327 | 0.1219 |
| 0.6993 | 0.75 | 60 | 0.4896 | 0.1086 | 0.3899 | 0.1699 | 0.7420 | 0.1086 | 0.3899 | 0.1699 |
| 0.6993 | 1.0 | 80 | 0.4554 | 0.1497 | 0.3145 | 0.2028 | 0.7840 | 0.1497 | 0.3145 | 0.2028 |
| 0.6993 | 1.25 | 100 | 0.4952 | 0.1980 | 0.3774 | 0.2597 | 0.8353 | 0.1980 | 0.3774 | 0.2597 |
| 0.6993 | 1.5 | 120 | 0.4837 | 0.1749 | 0.3774 | 0.2390 | 0.7984 | 0.1749 | 0.3774 | 0.2390 |
| 0.6993 | 1.75 | 140 | 0.4786 | 0.1873 | 0.4088 | 0.2569 | 0.7991 | 0.1873 | 0.4088 | 0.2569 |
| 0.6993 | 2.0 | 160 | 0.5157 | 0.2230 | 0.4277 | 0.2931 | 0.8241 | 0.2230 | 0.4277 | 0.2931 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.1.post100
- Datasets 2.20.0
- Tokenizers 0.15.1
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