---
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: 0.6
    - name: Precision
      type: precision
      value: 0.6000400160064026
    - name: Recall
      type: recall
      value: 0.6
    - name: F1
      type: f1
      value: 0.5999599959995999
---

<!-- 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. -->

# 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: 0.6634
- Accuracy: 0.6
- Precision: 0.6000
- Recall: 0.6
- F1: 0.6000

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.99  | 53   | 0.6935          | 0.515    | 0.5177    | 0.515  | 0.4958 |
| 0.7014        | 2.0   | 107  | 0.6895          | 0.535    | 0.5363    | 0.535  | 0.5308 |
| 0.7014        | 2.99  | 160  | 0.6894          | 0.52     | 0.5267    | 0.52   | 0.488  |
| 0.6961        | 4.0   | 214  | 0.6846          | 0.575    | 0.5842    | 0.575  | 0.5631 |
| 0.6961        | 4.99  | 267  | 0.6837          | 0.535    | 0.5931    | 0.535  | 0.4490 |
| 0.687         | 6.0   | 321  | 0.6762          | 0.585    | 0.5852    | 0.585  | 0.5847 |
| 0.687         | 6.99  | 374  | 0.6738          | 0.58     | 0.58      | 0.58   | 0.58   |
| 0.6707        | 8.0   | 428  | 0.6677          | 0.59     | 0.5900    | 0.59   | 0.5900 |
| 0.6707        | 8.99  | 481  | 0.6670          | 0.575    | 0.5767    | 0.575  | 0.5726 |
| 0.653         | 9.91  | 530  | 0.6634          | 0.6      | 0.6000    | 0.6    | 0.6000 |


### Framework versions

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