---
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