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---
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
model-index:
- name: ICU_Returns_ClinicalBERT
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. -->
# ICU_Returns_ClinicalBERT
This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3201
- F1:: 0.7134
- Roc Auc: 0.7225
- Precision with 0:: 0.8462
- Precision with 1:: 0.6640
- Recall with 0:: 0.5440
- Recal with 1:: 0.9011
- Accuracy:: 0.7225
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1: | Roc Auc | Precision with 0: | Precision with 1: | Recall with 0: | Recal with 1: | Accuracy: |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:---------:|
| No log | 1.0 | 46 | 0.7057 | 0.3454 | 0.5055 | 1.0 | 0.5028 | 0.0110 | 1.0 | 0.5055 |
| No log | 2.0 | 92 | 0.6827 | 0.5715 | 0.5742 | 0.5882 | 0.5640 | 0.4945 | 0.6538 | 0.5742 |
| No log | 3.0 | 138 | 0.7221 | 0.4612 | 0.5467 | 0.7297 | 0.5260 | 0.1484 | 0.9451 | 0.5467 |
| No log | 4.0 | 184 | 0.6284 | 0.6693 | 0.6841 | 0.6293 | 0.8190 | 0.8956 | 0.4725 | 0.6841 |
| No log | 5.0 | 230 | 0.9235 | 0.6283 | 0.6401 | 0.7179 | 0.6032 | 0.4615 | 0.8187 | 0.6401 |
| No log | 6.0 | 276 | 0.8772 | 0.6534 | 0.6648 | 0.7586 | 0.6210 | 0.4835 | 0.8462 | 0.6648 |
| No log | 7.0 | 322 | 0.7968 | 0.7677 | 0.7692 | 0.8224 | 0.7311 | 0.6868 | 0.8516 | 0.7692 |
| No log | 8.0 | 368 | 0.6826 | 0.8132 | 0.8132 | 0.8167 | 0.8098 | 0.8077 | 0.8187 | 0.8132 |
| No log | 9.0 | 414 | 1.2195 | 0.6950 | 0.7033 | 0.8033 | 0.6529 | 0.5385 | 0.8681 | 0.7033 |
| No log | 10.0 | 460 | 0.9542 | 0.7617 | 0.7637 | 0.8243 | 0.7222 | 0.6703 | 0.8571 | 0.7637 |
| 0.3635 | 11.0 | 506 | 1.3032 | 0.7079 | 0.7143 | 0.8047 | 0.6653 | 0.5659 | 0.8626 | 0.7143 |
| 0.3635 | 12.0 | 552 | 1.4170 | 0.7063 | 0.7143 | 0.8197 | 0.6612 | 0.5495 | 0.8791 | 0.7143 |
| 0.3635 | 13.0 | 598 | 1.3201 | 0.7134 | 0.7225 | 0.8462 | 0.6640 | 0.5440 | 0.9011 | 0.7225 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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