Model save

README.md

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+---
+library_name: peft
+license: mit
+base_model: microsoft/phi-2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: phi-2-heart
+  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. -->
+
+# phi-2-heart
+
+This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6017
+- Accuracy: 0.6667
+- Report:               precision    recall  f1-score   support
+
+     absence       0.63      0.98      0.77        45
+    presence       0.91      0.28      0.43        36
+
+    accuracy                           0.67        81
+   macro avg       0.77      0.63      0.60        81
+weighted avg       0.75      0.67      0.61        81
+
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Report                                                                                                                                                                                                                                                                                                                                 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 48   | 0.7215          | 0.5556   |               precision    recall  f1-score   support
+
+     absence       0.56      1.00      0.71        45
+    presence       0.00      0.00      0.00        36
+
+    accuracy                           0.56        81
+   macro avg       0.28      0.50      0.36        81
+weighted avg       0.31      0.56      0.40        81
+ |
+| No log        | 2.0   | 96   | 0.6567          | 0.5802   |               precision    recall  f1-score   support
+
+     absence       0.57      1.00      0.73        45
+    presence       1.00      0.06      0.11        36
+
+    accuracy                           0.58        81
+   macro avg       0.78      0.53      0.42        81
+weighted avg       0.76      0.58      0.45        81
+ |
+| No log        | 3.0   | 144  | 0.6312          | 0.6667   |               precision    recall  f1-score   support
+
+     absence       0.63      0.96      0.76        45
+    presence       0.85      0.31      0.45        36
+
+    accuracy                           0.67        81
+   macro avg       0.74      0.63      0.61        81
+weighted avg       0.73      0.67      0.62        81
+ |
+| No log        | 4.0   | 192  | 0.6253          | 0.5926   |               precision    recall  f1-score   support
+
+     absence       0.58      1.00      0.73        45
+    presence       1.00      0.08      0.15        36
+
+    accuracy                           0.59        81
+   macro avg       0.79      0.54      0.44        81
+weighted avg       0.76      0.59      0.47        81
+ |
+| No log        | 5.0   | 240  | 0.6017          | 0.6667   |               precision    recall  f1-score   support
+
+     absence       0.63      0.98      0.77        45
+    presence       0.91      0.28      0.43        36
+
+    accuracy                           0.67        81
+   macro avg       0.77      0.63      0.60        81
+weighted avg       0.75      0.67      0.61        81
+ |
+
+
+### Framework versions
+
+- PEFT 0.15.2
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 2.14.4
+- Tokenizers 0.21.1

runs/May20_22-47-02_711b6d5ce6b0/events.out.tfevents.1747781655.711b6d5ce6b0.376.1

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