---
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: run1
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sem_eval_2024_task_2
      type: sem_eval_2024_task_2
      config: sem_eval_2024_task_2_source
      split: validation
      args: sem_eval_2024_task_2_source
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.575
    - name: Precision
      type: precision
      value: 0.5800000000000001
    - name: Recall
      type: recall
      value: 0.575
    - name: F1
      type: f1
      value: 0.5682539682539682
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# run1

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2723
- Accuracy: 0.575
- Precision: 0.5800
- Recall: 0.575
- F1: 0.5683

## 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: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.99  | 53   | 0.6960          | 0.52     | 0.5339    | 0.52   | 0.4652 |
| 0.7039        | 2.0   | 107  | 0.6921          | 0.545    | 0.5451    | 0.5450 | 0.5447 |
| 0.7039        | 2.99  | 160  | 0.6956          | 0.515    | 0.5436    | 0.515  | 0.4198 |
| 0.6979        | 4.0   | 214  | 0.6857          | 0.555    | 0.5587    | 0.555  | 0.5479 |
| 0.6979        | 4.99  | 267  | 0.6924          | 0.51     | 0.7525    | 0.51   | 0.3552 |
| 0.6831        | 6.0   | 321  | 0.6603          | 0.575    | 0.5750    | 0.575  | 0.5750 |
| 0.6831        | 6.99  | 374  | 0.6572          | 0.61     | 0.6116    | 0.6100 | 0.6086 |
| 0.6346        | 8.0   | 428  | 0.6517          | 0.57     | 0.5700    | 0.5700 | 0.5700 |
| 0.6346        | 8.99  | 481  | 0.7185          | 0.58     | 0.5849    | 0.58   | 0.5739 |
| 0.5337        | 10.0  | 535  | 0.8220          | 0.565    | 0.5767    | 0.565  | 0.5478 |
| 0.5337        | 10.99 | 588  | 0.8002          | 0.595    | 0.5958    | 0.595  | 0.5942 |
| 0.4262        | 12.0  | 642  | 0.8661          | 0.595    | 0.5994    | 0.595  | 0.5905 |
| 0.4262        | 12.99 | 695  | 0.9989          | 0.555    | 0.5608    | 0.5550 | 0.5440 |
| 0.3379        | 14.0  | 749  | 1.0688          | 0.56     | 0.5651    | 0.5600 | 0.5512 |
| 0.3379        | 14.99 | 802  | 1.0439          | 0.585    | 0.5865    | 0.585  | 0.5832 |
| 0.2846        | 16.0  | 856  | 1.1091          | 0.575    | 0.5809    | 0.575  | 0.5671 |
| 0.2846        | 16.99 | 909  | 1.2667          | 0.57     | 0.5791    | 0.5700 | 0.5572 |
| 0.228         | 18.0  | 963  | 1.2367          | 0.58     | 0.5858    | 0.58   | 0.5728 |
| 0.228         | 18.99 | 1016 | 1.2373          | 0.585    | 0.5889    | 0.585  | 0.5804 |
| 0.2137        | 19.81 | 1060 | 1.2723          | 0.575    | 0.5800    | 0.575  | 0.5683 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0