---
license: mit
base_model: hongpingjun98/BioMedNLP_DeBERTa
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: BioMedNLP_DeBERTa_all_updates
  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: 0.655
    - name: Precision
      type: precision
      value: 0.6714791459232217
    - name: Recall
      type: recall
      value: 0.655
    - name: F1
      type: f1
      value: 0.6465073388150311
---

<!-- 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. -->

# BioMedNLP_DeBERTa_all_updates

This model is a fine-tuned version of [hongpingjun98/BioMedNLP_DeBERTa](https://huggingface.co/hongpingjun98/BioMedNLP_DeBERTa) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4673
- Accuracy: 0.655
- Precision: 0.6715
- Recall: 0.655
- F1: 0.6465

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3757        | 1.0   | 115  | 0.6988          | 0.7      | 0.7020    | 0.7    | 0.6992 |
| 0.3965        | 2.0   | 230  | 0.7320          | 0.695    | 0.7259    | 0.6950 | 0.6842 |
| 0.3603        | 3.0   | 345  | 0.7736          | 0.7      | 0.7338    | 0.7    | 0.6888 |
| 0.2721        | 4.0   | 460  | 0.8780          | 0.665    | 0.6802    | 0.665  | 0.6578 |
| 0.4003        | 5.0   | 575  | 0.9046          | 0.655    | 0.6796    | 0.655  | 0.6428 |
| 0.2773        | 6.0   | 690  | 0.9664          | 0.7      | 0.7053    | 0.7    | 0.6981 |
| 0.2465        | 7.0   | 805  | 1.0035          | 0.67     | 0.6845    | 0.67   | 0.6634 |
| 0.3437        | 8.0   | 920  | 1.0087          | 0.665    | 0.6780    | 0.665  | 0.6588 |
| 0.1175        | 9.0   | 1035 | 1.2598          | 0.675    | 0.6780    | 0.675  | 0.6736 |
| 0.155         | 10.0  | 1150 | 1.3976          | 0.69     | 0.7038    | 0.69   | 0.6847 |
| 0.1013        | 11.0  | 1265 | 1.3761          | 0.67     | 0.6757    | 0.6700 | 0.6673 |
| 0.1664        | 12.0  | 1380 | 1.5027          | 0.695    | 0.6950    | 0.695  | 0.6950 |
| 0.0847        | 13.0  | 1495 | 1.8199          | 0.685    | 0.6973    | 0.685  | 0.68   |
| 0.0856        | 14.0  | 1610 | 1.8299          | 0.66     | 0.6783    | 0.6600 | 0.6511 |
| 0.1053        | 15.0  | 1725 | 2.0431          | 0.665    | 0.6852    | 0.665  | 0.6556 |
| 0.0958        | 16.0  | 1840 | 1.9203          | 0.7      | 0.7040    | 0.7    | 0.6985 |
| 0.0344        | 17.0  | 1955 | 2.1390          | 0.665    | 0.6780    | 0.665  | 0.6588 |
| 0.014         | 18.0  | 2070 | 2.3609          | 0.655    | 0.6692    | 0.655  | 0.6476 |
| 0.0085        | 19.0  | 2185 | 2.4310          | 0.65     | 0.6671    | 0.65   | 0.6408 |
| 0.0285        | 20.0  | 2300 | 2.4673          | 0.655    | 0.6715    | 0.655  | 0.6465 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0