---
base_model: medicalai/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: 0.595
    - name: Precision
      type: precision
      value: 0.632109581421221
    - name: Recall
      type: recall
      value: 0.595
    - name: F1
      type: f1
      value: 0.5644107445349681
---

<!-- 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 [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6989
- Accuracy: 0.595
- Precision: 0.6321
- Recall: 0.595
- F1: 0.5644

## 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   | 0.6932          | 0.5      | 0.5       | 0.5    | 0.4302 |
| 0.6952        | 2.0   | 107  | 0.6946          | 0.505    | 0.5059    | 0.505  | 0.4854 |
| 0.6952        | 2.99  | 160  | 0.6938          | 0.485    | 0.4127    | 0.485  | 0.3505 |
| 0.6953        | 4.0   | 214  | 0.6937          | 0.5      | 0.5       | 0.5    | 0.4389 |
| 0.6953        | 4.99  | 267  | 0.6961          | 0.5      | 0.25      | 0.5    | 0.3333 |
| 0.6937        | 6.0   | 321  | 0.6936          | 0.5      | 0.25      | 0.5    | 0.3333 |
| 0.6937        | 6.99  | 374  | 0.6908          | 0.495    | 0.4487    | 0.495  | 0.3479 |
| 0.6927        | 8.0   | 428  | 0.6804          | 0.545    | 0.5485    | 0.545  | 0.5366 |
| 0.6927        | 8.99  | 481  | 0.6888          | 0.525    | 0.5535    | 0.525  | 0.4520 |
| 0.6799        | 10.0  | 535  | 0.6657          | 0.615    | 0.6476    | 0.615  | 0.5925 |
| 0.6799        | 10.99 | 588  | 0.6600          | 0.625    | 0.6448    | 0.625  | 0.6117 |
| 0.6509        | 12.0  | 642  | 0.6598          | 0.595    | 0.6407    | 0.595  | 0.5592 |
| 0.6509        | 12.99 | 695  | 0.6598          | 0.605    | 0.6555    | 0.605  | 0.5701 |
| 0.6122        | 14.0  | 749  | 0.6643          | 0.59     | 0.6234    | 0.59   | 0.5603 |
| 0.6122        | 14.99 | 802  | 0.6754          | 0.605    | 0.6818    | 0.605  | 0.5584 |
| 0.5601        | 16.0  | 856  | 0.6788          | 0.605    | 0.6382    | 0.605  | 0.5798 |
| 0.5601        | 16.99 | 909  | 0.6864          | 0.59     | 0.6234    | 0.59   | 0.5603 |
| 0.5159        | 18.0  | 963  | 0.6967          | 0.6      | 0.6457    | 0.6    | 0.5660 |
| 0.5159        | 18.99 | 1016 | 0.7037          | 0.6      | 0.6507    | 0.6    | 0.5633 |
| 0.5117        | 19.81 | 1060 | 0.6989          | 0.595    | 0.6321    | 0.595  | 0.5644 |


### Framework versions

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