DeBERTaV3_model_multilabel

This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0221
• Accuracy: 0.9919
• F1: 0.3922
• Precision: 0.6667
• Recall: 0.2778

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: 5
• eval_batch_size: 5
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	25	0.4442	0.9516	0.1475	0.0884	0.4444
No log	2.0	50	0.1757	0.9919	0.3922	0.6667	0.2778
No log	3.0	75	0.0655	0.9919	0.3922	0.6667	0.2778
No log	4.0	100	0.0378	0.9919	0.3922	0.6667	0.2778
No log	5.0	125	0.0292	0.9919	0.3922	0.6667	0.2778
No log	6.0	150	0.0255	0.9919	0.3922	0.6667	0.2778
No log	7.0	175	0.0238	0.9919	0.3922	0.6667	0.2778
No log	8.0	200	0.0227	0.9919	0.3922	0.6667	0.2778
No log	9.0	225	0.0222	0.9919	0.3922	0.6667	0.2778
No log	10.0	250	0.0221	0.9919	0.3922	0.6667	0.2778

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1