| | --- |
| | 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 |
| | |
