--- 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: [] --- # 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