rishavranaut/QWEN_with_time

README.md

@@ -0,0 +1,101 @@
+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: Qwen/Qwen2-7B
+metrics:
+- accuracy
+model-index:
+- name: QWEN_with_time
+  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. -->
+
+# QWEN_with_time
+
+This model is a fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3495
+- Balanced Accuracy: 0.8801
+- Accuracy: 0.8801
+- Micro F1: 0.8801
+- Macro F1: 0.8800
+- Weighted F1: 0.8800
+- Classification Report:               precision    recall  f1-score   support
+
+           0       0.89      0.87      0.88       386
+           1       0.87      0.89      0.88       381
+
+    accuracy                           0.88       767
+   macro avg       0.88      0.88      0.88       767
+weighted avg       0.88      0.88      0.88       767
+
+
+## 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: 0.0001
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Balanced Accuracy | Accuracy | Micro F1 | Macro F1 | Weighted F1 | Classification Report                                                                                                                                                                                                                                                                                                                  |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|:--------:|:--------:|:-----------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 0.6244        | 1.0   | 384  | 0.4657          | 0.8336            | 0.8331   | 0.8331   | 0.8323   | 0.8322      |               precision    recall  f1-score   support
+
+           0       0.89      0.76      0.82       386
+           1       0.79      0.91      0.84       381
+
+    accuracy                           0.83       767
+   macro avg       0.84      0.83      0.83       767
+weighted avg       0.84      0.83      0.83       767
+ |
+| 0.3513        | 2.0   | 768  | 0.4065          | 0.8639            | 0.8644   | 0.8644   | 0.8634   | 0.8635      |               precision    recall  f1-score   support
+
+           0       0.82      0.94      0.88       386
+           1       0.93      0.78      0.85       381
+
+    accuracy                           0.86       767
+   macro avg       0.87      0.86      0.86       767
+weighted avg       0.87      0.86      0.86       767
+ |
+| 0.2858        | 3.0   | 1152 | 0.3495          | 0.8801            | 0.8801   | 0.8801   | 0.8800   | 0.8800      |               precision    recall  f1-score   support
+
+           0       0.89      0.87      0.88       386
+           1       0.87      0.89      0.88       381
+
+    accuracy                           0.88       767
+   macro avg       0.88      0.88      0.88       767
+weighted avg       0.88      0.88      0.88       767
+ |
+
+
+### Framework versions
+
+- PEFT 0.11.1
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1