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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: nli-subgroups-target-abroad |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# nli-subgroups-target-abroad |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3895 |
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- Accuracy: 0.9270 |
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- Precision Binary: 0.5491 |
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- Recall Binary: 0.6333 |
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- F1 Binary: 0.5882 |
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- Precision Micro: 0.9270 |
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- Recall Micro: 0.9270 |
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- F1 Micro: 0.9270 |
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- Pr Auc: 0.5737 |
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- Cohen Kappa: 0.5484 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Binary | Recall Binary | F1 Binary | Precision Micro | Recall Micro | F1 Micro | Pr Auc | Cohen Kappa | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:---------------:|:------------:|:--------:|:------:|:-----------:| |
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| No log | 1.0 | 456 | 1.0509 | 0.9237 | 0.5307 | 0.6333 | 0.5775 | 0.9237 | 0.9237 | 0.9237 | 0.5397 | 0.5359 | |
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| 1.1638 | 2.0 | 912 | 0.7448 | 0.9303 | 0.5782 | 0.5667 | 0.5724 | 0.9303 | 0.9303 | 0.9303 | 0.5361 | 0.5345 | |
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| 0.9414 | 3.0 | 1368 | 1.3895 | 0.9270 | 0.5491 | 0.6333 | 0.5882 | 0.9270 | 0.9270 | 0.9270 | 0.5737 | 0.5484 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 2.14.4 |
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- Tokenizers 0.21.1 |
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