---
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ClinicalBERT-medical-text-classification
  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. -->

# ClinicalBERT-medical-text-classification

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: 2.0223
- Accuracy: 0.273
- Precision: 0.2079
- Recall: 0.273
- F1: 0.2270

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.5449        | 1.0   | 250  | 2.5124          | 0.377    | 0.1879    | 0.377  | 0.2437 |
| 2.0748        | 2.0   | 500  | 2.0375          | 0.372    | 0.3107    | 0.372  | 0.2994 |
| 1.766         | 3.0   | 750  | 1.8367          | 0.351    | 0.3350    | 0.351  | 0.3148 |
| 1.6599        | 4.0   | 1000 | 1.8015          | 0.303    | 0.2597    | 0.303  | 0.2703 |
| 1.4441        | 5.0   | 1250 | 1.9012          | 0.308    | 0.2483    | 0.308  | 0.2634 |
| 1.4366        | 6.0   | 1500 | 1.9159          | 0.253    | 0.2412    | 0.253  | 0.2390 |
| 1.1871        | 7.0   | 1750 | 2.0223          | 0.273    | 0.2079    | 0.273  | 0.2270 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2