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mdeberta-v3-base-finetuned-climate-activism
mljn/mdeberta-v3-base-finetuned-climate-activism
mdeberta-v3-base-finetuned-climate-activism 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 23, 2025
- Updated
- Nov 23, 2025
- SHA
- d380fabc
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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-activism
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: 0.1116
- Accuracy: 0.9811
- Accuracy Balanced: 0.9474
- F1 Macro: 0.9665
- F1 Weighted: 0.9807
- F1 Micro: 0.9811
- F1 Positive: 0.9444
- Precision Macro: 0.9888
- Precision Weighted: 0.9816
- Recall Macro: 0.9474
- Recall Weighted: 0.9811
- Mcc: 0.9352
- Roc Auc: 0.9874
- Pr Auc: 0.9813
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 124 | 0.2669 | 0.8815 | 0.6834 | 0.7303 | 0.8614 | 0.8815 | 0.5283 | 0.8785 | 0.8808 | 0.6834 | 0.8815 | 0.5270 | 0.9489 | 0.8435 |
| No log | 2.0 | 248 | 0.2180 | 0.9431 | 0.9230 | 0.9056 | 0.9443 | 0.9431 | 0.8462 | 0.8907 | 0.9464 | 0.9230 | 0.9431 | 0.8130 | 0.9784 | 0.9183 |
| No log | 3.0 | 372 | 0.1975 | 0.9621 | 0.9132 | 0.9315 | 0.9612 | 0.9621 | 0.8857 | 0.9528 | 0.9616 | 0.9132 | 0.9621 | 0.8651 | 0.9875 | 0.9555 |
| No log | 4.0 | 496 | 0.3245 | 0.9336 | 0.9385 | 0.8960 | 0.9366 | 0.9336 | 0.8333 | 0.8662 | 0.9452 | 0.9385 | 0.9336 | 0.8015 | 0.9833 | 0.9378 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Nov 23, 2025
- Updated
- Nov 23, 2025