---
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: 0.705
    - name: Precision
      type: precision
      value: 0.7238235615241838
    - name: Recall
      type: recall
      value: 0.7050000000000001
    - name: F1
      type: f1
      value: 0.6986644194182692
---

<!-- 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: 2.1863
- Accuracy: 0.705
- Precision: 0.7238
- Recall: 0.7050
- F1: 0.6987

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4238        | 1.0   | 116  | 0.6639          | 0.665    | 0.6678    | 0.665  | 0.6636 |
| 0.4316        | 2.0   | 232  | 0.6644          | 0.68     | 0.6875    | 0.6800 | 0.6768 |
| 0.3819        | 3.0   | 348  | 0.7328          | 0.71     | 0.7188    | 0.71   | 0.7071 |
| 0.3243        | 4.0   | 464  | 0.9162          | 0.7      | 0.7083    | 0.7    | 0.6970 |
| 0.4053        | 5.0   | 580  | 0.7145          | 0.715    | 0.7214    | 0.7150 | 0.7129 |
| 0.2548        | 6.0   | 696  | 1.0598          | 0.69     | 0.7016    | 0.69   | 0.6855 |
| 0.3455        | 7.0   | 812  | 0.7782          | 0.72     | 0.7232    | 0.72   | 0.7190 |
| 0.2177        | 8.0   | 928  | 1.1182          | 0.69     | 0.6950    | 0.69   | 0.6880 |
| 0.2304        | 9.0   | 1044 | 1.4332          | 0.695    | 0.708     | 0.695  | 0.6902 |
| 0.2103        | 10.0  | 1160 | 1.2736          | 0.7      | 0.7198    | 0.7    | 0.6931 |
| 0.1748        | 11.0  | 1276 | 1.2654          | 0.675    | 0.6816    | 0.675  | 0.6720 |
| 0.1608        | 12.0  | 1392 | 1.8885          | 0.63     | 0.6689    | 0.63   | 0.6074 |
| 0.1082        | 13.0  | 1508 | 1.7004          | 0.68     | 0.7005    | 0.6800 | 0.6716 |
| 0.1074        | 14.0  | 1624 | 1.8145          | 0.67     | 0.6804    | 0.67   | 0.6652 |
| 0.0238        | 15.0  | 1740 | 1.7608          | 0.68     | 0.6931    | 0.68   | 0.6745 |
| 0.038         | 16.0  | 1856 | 1.9937          | 0.67     | 0.6953    | 0.6700 | 0.6589 |
| 0.0365        | 17.0  | 1972 | 2.1871          | 0.675    | 0.6964    | 0.675  | 0.6659 |
| 0.0144        | 18.0  | 2088 | 2.1093          | 0.695    | 0.7059    | 0.6950 | 0.6909 |
| 0.0014        | 19.0  | 2204 | 2.1559          | 0.695    | 0.7103    | 0.6950 | 0.6893 |
| 0.0324        | 20.0  | 2320 | 2.1863          | 0.705    | 0.7238    | 0.7050 | 0.6987 |


### Framework versions

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