metadata

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

BioMedNLP_DeBERTa_all_updates

This model is a fine-tuned version of 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