mdeberta-v3-base-finetuned-climate-stance-classification – AI Model by mljn | AlphaNeural AI

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mljn/
mdeberta-v3-base-finetuned-climate-stance-classification

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text-classification

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microsoft/mdeberta-v3-base

autotrain_compatible

endpoints_compatible

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mdeberta-v3-base-finetuned-climate-stance-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: 0.7136
Accuracy: 0.8425
F1 Macro: 0.5001
Accuracy Balanced: 0.4849
F1 Micro: 0.8425
Precision Macro: 0.5547
Recall Macro: 0.4849
Precision Micro: 0.8425
Recall Micro: 0.8425

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
0.7463	0.9960	500	0.6655	0.7906	0.4032	0.4355	0.7906	0.3850	0.4355	0.7906	0.7906
0.4669	1.9920	1000	0.5744	0.8405	0.4573	0.4529	0.8405	0.6684	0.4529	0.8405	0.8405
0.3614	2.9880	1500	0.6516	0.8504	0.4952	0.4816	0.8504	0.5977	0.4816	0.8504	0.8504
0.2738	3.9841	2000	0.7136	0.8425	0.5001	0.4849	0.8425	0.5547	0.4849	0.8425	0.8425

Framework versions

Transformers 4.52.2
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1