| --- |
| library_name: transformers |
| base_model: UBC-NLP/MARBERTv2 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: checkpoints |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # checkpoints |
|
|
| This model is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.1854 |
| - Accuracy: 0.7595 |
| - Balanced Accuracy: 0.7595 |
| - Mcc: 0.6402 |
| - Macro F1: 0.7607 |
| - Macro Precision: 0.7646 |
| - Macro Recall: 0.7595 |
| - Weighted F1: 0.7607 |
| - Weighted Precision: 0.7646 |
| - Weighted Recall: 0.7595 |
| - Class 0 F1: 0.8113 |
| - Class 0 Precision: 0.86 |
| - Class 0 Recall: 0.7679 |
| - Class 1 F1: 0.7235 |
| - Class 1 Precision: 0.7177 |
| - Class 1 Recall: 0.7293 |
| - Class 2 F1: 0.7473 |
| - Class 2 Precision: 0.7162 |
| - Class 2 Recall: 0.7812 |
|
|
| ## 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: 16 |
| - eval_batch_size: 32 |
| - 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_steps: 0.1 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced Accuracy | Mcc | Macro F1 | Macro Precision | Macro Recall | Weighted F1 | Weighted Precision | Weighted Recall | Class 0 F1 | Class 0 Precision | Class 0 Recall | Class 1 F1 | Class 1 Precision | Class 1 Recall | Class 2 F1 | Class 2 Precision | Class 2 Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:------:|:--------:|:---------------:|:------------:|:-----------:|:------------------:|:---------------:|:----------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:| |
| | 0.5611 | 1.0 | 925 | 0.5775 | 0.7714 | 0.7714 | 0.6584 | 0.7699 | 0.7716 | 0.7714 | 0.7699 | 0.7716 | 0.7714 | 0.8281 | 0.8052 | 0.8523 | 0.7215 | 0.7737 | 0.6759 | 0.7602 | 0.7360 | 0.7861 | |
| | 0.4367 | 2.0 | 1850 | 0.6233 | 0.7757 | 0.7757 | 0.6641 | 0.7759 | 0.7774 | 0.7757 | 0.7759 | 0.7774 | 0.7757 | 0.8223 | 0.8418 | 0.8036 | 0.7355 | 0.7504 | 0.7212 | 0.7698 | 0.7399 | 0.8023 | |
| | 0.2949 | 3.0 | 2775 | 0.7892 | 0.7703 | 0.7703 | 0.6573 | 0.7717 | 0.7778 | 0.7703 | 0.7717 | 0.7777 | 0.7703 | 0.8132 | 0.8748 | 0.7597 | 0.7473 | 0.7044 | 0.7958 | 0.7547 | 0.7540 | 0.7553 | |
| | 0.2141 | 4.0 | 3700 | 1.0209 | 0.7584 | 0.7584 | 0.6389 | 0.7599 | 0.7646 | 0.7584 | 0.7599 | 0.7645 | 0.7584 | 0.8093 | 0.8595 | 0.7646 | 0.7281 | 0.6920 | 0.7682 | 0.7423 | 0.7423 | 0.7423 | |
| | 0.1405 | 5.0 | 4625 | 1.1854 | 0.7595 | 0.7595 | 0.6402 | 0.7607 | 0.7646 | 0.7595 | 0.7607 | 0.7646 | 0.7595 | 0.8113 | 0.86 | 0.7679 | 0.7235 | 0.7177 | 0.7293 | 0.7473 | 0.7162 | 0.7812 | |
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|
| ### Framework versions |
|
|
| - Transformers 5.13.1 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
