---
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