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mdeberta-v3-base-finetuned-climate-implicit-classification-full
mljn/mdeberta-v3-base-finetuned-climate-implicit-classification-full
mdeberta-v3-base-finetuned-climate-implicit-classification-full is a text classification model from mljn. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- Nov 11, 2025
- Updated
- Nov 11, 2025
- SHA
- 1f40d5db
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
mdeberta-v3-base-finetuned-climate-implicit-classification-full
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: 0.2690
- Accuracy: 0.9488
- Accuracy Balanced: 0.9119
- F1 Macro: 0.9063
- F1 Weighted: 0.9491
- F1 Micro: 0.9488
- F1 Positive: 0.8432
- Precision Macro: 0.9008
- Precision Weighted: 0.9496
- Recall Macro: 0.9119
- Recall Weighted: 0.9488
- Mcc: 0.8127
- Roc Auc: 0.9582
- Pr Auc: 0.8947
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_FUSED 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy Balanced | F1 Macro | F1 Weighted | F1 Micro | F1 Positive | Precision Macro | Precision Weighted | Recall Macro | Recall Weighted | Mcc | Roc Auc | Pr Auc |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.2666 | 1.0 | 1787 | 0.2213 | 0.9370 | 0.8916 | 0.8850 | 0.9375 | 0.9370 | 0.8076 | 0.8787 | 0.9382 | 0.8916 | 0.9370 | 0.7702 | 0.9430 | 0.8353 |
| 0.1741 | 2.0 | 3574 | 0.2057 | 0.9497 | 0.8893 | 0.9032 | 0.9488 | 0.9497 | 0.8362 | 0.9189 | 0.9485 | 0.8893 | 0.9497 | 0.8077 | 0.9654 | 0.9041 |
| 0.1184 | 3.0 | 5361 | 0.2107 | 0.9569 | 0.9150 | 0.9192 | 0.9567 | 0.9569 | 0.8639 | 0.9234 | 0.9565 | 0.9150 | 0.9569 | 0.8384 | 0.9657 | 0.8972 |
| 0.0859 | 4.0 | 7148 | 0.2223 | 0.9569 | 0.9315 | 0.9218 | 0.9574 | 0.9569 | 0.8693 | 0.9127 | 0.9581 | 0.9315 | 0.9569 | 0.8440 | 0.9686 | 0.8970 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Nov 11, 2025
- Updated
- Nov 11, 2025