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---
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
|