| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: answerdotai/ModernBERT-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: kpi-priority-model |
| 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. --> |
|
|
| # kpi-priority-model |
|
|
| This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7414 |
| - Accuracy: 0.7288 |
| - F1: 0.7266 |
| - Classification Report: precision recall f1-score support |
|
|
| Low 0.68 0.83 0.75 94 |
| Medium 0.66 0.52 0.58 111 |
| High 0.69 0.73 0.71 202 |
| Critical 0.81 0.78 0.80 253 |
| |
| accuracy 0.73 660 |
| macro avg 0.71 0.72 0.71 660 |
| weighted avg 0.73 0.73 0.73 660 |
| |
|
|
| ## 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 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - 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: 100 |
| - num_epochs: 2 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Classification Report | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
| | 2.3852 | 1.0 | 165 | 0.8176 | 0.6833 | 0.6838 | precision recall f1-score support |
| |
| Low 0.65 0.85 0.74 94 |
| Medium 0.55 0.55 0.55 111 |
| High 0.62 0.78 0.69 202 |
| Critical 0.88 0.60 0.72 253 |
| |
| accuracy 0.68 660 |
| macro avg 0.68 0.70 0.67 660 |
| weighted avg 0.71 0.68 0.68 660 |
| | |
| | 1.3946 | 2.0 | 330 | 0.7414 | 0.7288 | 0.7266 | precision recall f1-score support |
| |
| Low 0.68 0.83 0.75 94 |
| Medium 0.66 0.52 0.58 111 |
| High 0.69 0.73 0.71 202 |
| Critical 0.81 0.78 0.80 253 |
| |
| accuracy 0.73 660 |
| macro avg 0.71 0.72 0.71 660 |
| weighted avg 0.73 0.73 0.73 660 |
| | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.49.0.dev0 |
| - Pytorch 2.5.1+cu121 |
| - Datasets 3.2.0 |
| - Tokenizers 0.21.0 |
| |
