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---
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
model-index:
- name: ICU_Returns_BioClinicalBERT
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_BioClinicalBERT
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7775
- F1:: 0.7063
- Roc Auc: 0.7198
- Precision with 0:: 0.8846
- Precision with 1:: 0.6538
- Recall with 0:: 0.5055
- Recal with 1:: 0.9341
- Accuracy:: 0.7198
## 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 | 0.6964 | 0.3573 | 0.5110 | 1.0 | 0.5056 | 0.0220 | 1.0 | 0.5110 |
| No log | 2.0 | 92 | 0.6611 | 0.5248 | 0.5714 | 0.6912 | 0.5439 | 0.2582 | 0.8846 | 0.5714 |
| No log | 3.0 | 138 | 0.6322 | 0.6315 | 0.6374 | 0.6838 | 0.6096 | 0.5110 | 0.7637 | 0.6374 |
| No log | 4.0 | 184 | 0.6526 | 0.6396 | 0.6566 | 0.7767 | 0.6092 | 0.4396 | 0.8736 | 0.6566 |
| No log | 5.0 | 230 | 0.6826 | 0.6693 | 0.6923 | 0.9070 | 0.6259 | 0.4286 | 0.9560 | 0.6923 |
| No log | 6.0 | 276 | 0.7496 | 0.7230 | 0.7335 | 0.8829 | 0.6680 | 0.5385 | 0.9286 | 0.7335 |
| No log | 7.0 | 322 | 1.5500 | 0.6398 | 0.6703 | 0.9079 | 0.6076 | 0.3791 | 0.9615 | 0.6703 |
| No log | 8.0 | 368 | 0.9037 | 0.7438 | 0.7527 | 0.9035 | 0.684 | 0.5659 | 0.9396 | 0.7527 |
| No log | 9.0 | 414 | 1.6723 | 0.6965 | 0.7143 | 0.9149 | 0.6444 | 0.4725 | 0.9560 | 0.7143 |
| No log | 10.0 | 460 | 1.4913 | 0.7030 | 0.7170 | 0.8835 | 0.6513 | 0.5 | 0.9341 | 0.7170 |
| 0.3158 | 11.0 | 506 | 1.7129 | 0.6990 | 0.7143 | 0.89 | 0.6477 | 0.4890 | 0.9396 | 0.7143 |
| 0.3158 | 12.0 | 552 | 1.8420 | 0.6882 | 0.7060 | 0.8947 | 0.6394 | 0.4670 | 0.9451 | 0.7060 |
| 0.3158 | 13.0 | 598 | 1.7775 | 0.7063 | 0.7198 | 0.8846 | 0.6538 | 0.5055 | 0.9341 | 0.7198 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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