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: 2.1863
• 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