Model save

README.md

@@ -0,0 +1,98 @@
+---
+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