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README.md

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+---
+tags:
+- hojjatk/mnist-dataset
+- handwriting-recognition
+- classification
+- deep-learning
+metrics:
+  accuracy: '0.98'
+  precision: '0.98'
+  recall: '0.98'
+dataset:
+  name: hojjatk/mnist-dataset
+  type: image
+license: mit
+downloads:
+  count: 0
+---
+
+        # Handwriting Recognition Model
+        This is a trained model for handwriting recognition using **hojjatk/mnist-dataset** dataset.
+
+        ## Usage
+        ```python
+        model = torch.load("mnsit_digit_nn")
+        model.eval()
+        ```
+
+        ## Training Param:
+        epochs = 300
+        batch_size = 64
+        learning_rate = 0.001
+
+        ## Model Architectue:
+        ['(fc1): Linear(in_features=784, out_features=128, bias=True)', '(fc2): Linear(in_features=128, out_features=64, bias=True)', '(fc3): Linear(in_features=64, out_features=10, bias=True)', '(relu): ReLU()', '(dropout): Dropout(p=0.2, inplace=False)']
+        
+        ## Evaluation Results
+        - Accuracy: 0.98
+        - Precision: 0.98
+        - Recall: 0.98
+        

results.json

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+{"dataset": "hojjatk/mnist-dataset", "accuracy": 0.9829, "precision": 0.9827804009647189, "recall": 0.982794697841328, "eval_datetime": "2024-12-25T19:29:49.930195"}