mdeberta-v3-base-finetuned-climate-stance-opposed-classification

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

• Loss: 0.5341
• Accuracy: 0.8873
• F1 Macro: 0.8678
• Accuracy Balanced: 0.8625
• F1 Micro: 0.8873
• Precision Macro: 0.8738
• Recall Macro: 0.8625
• Precision Micro: 0.8873
• Recall Micro: 0.8873

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Accuracy Balanced	F1 Micro	Precision Macro	Recall Macro	Precision Micro	Recall Micro
0.4971	0.9960	500	0.3347	0.8674	0.8462	0.8454	0.8674	0.8471	0.8454	0.8674	0.8674
0.3153	1.9920	1000	0.4414	0.8833	0.8659	0.8681	0.8833	0.8638	0.8681	0.8833	0.8833
0.2104	2.9880	1500	0.5379	0.8843	0.8641	0.8586	0.8843	0.8705	0.8586	0.8843	0.8843
0.1374	3.9841	2000	0.5341	0.8873	0.8678	0.8625	0.8873	0.8738	0.8625	0.8873	0.8873

Framework versions

• Transformers 4.52.2
• Pytorch 2.6.0+cu124
• Datasets 2.14.4
• Tokenizers 0.21.1