---
library_name: transformers
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
  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. -->

# results

This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8614
- Accuracy: 0.6145
- Precision: 0.6243
- Recall: 0.6145
- F1: 0.5971
- Roc Auc: 0.8073

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 1.0   | 42   | 1.0433          | 0.4458   | 0.4351    | 0.4458 | 0.3685 | 0.7162  |
| No log        | 2.0   | 84   | 0.8946          | 0.5663   | 0.5641    | 0.5663 | 0.5559 | 0.7823  |
| No log        | 3.0   | 126  | 0.9142          | 0.5783   | 0.6385    | 0.5783 | 0.5332 | 0.7896  |
| No log        | 4.0   | 168  | 0.8497          | 0.6386   | 0.6434    | 0.6386 | 0.6299 | 0.8084  |
| No log        | 5.0   | 210  | 0.8614          | 0.6145   | 0.6243    | 0.6145 | 0.5971 | 0.8073  |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.5
- Tokenizers 0.20.3