Create app.py

app.py

@@ -0,0 +1,35 @@
+import torch
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset('your_dataset_name')
+
+# Initialize the model and processor
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
+
+# Prepare the dataset for training
+def preprocess_data(example):
+    pixel_values = processor(images=example['image'], return_tensors="pt").pixel_values
+    labels = processor(text=example['text'], return_tensors="pt").input_ids
+    return {'pixel_values': pixel_values, 'labels': labels}
+
+train_dataset = dataset['train'].map(preprocess_data)
+
+# Fine-tune the model
+training_args = {
+    'per_device_train_batch_size': 8,
+    'num_train_epochs': 3,
+    'logging_steps': 100,
+    'save_steps': 500,
+    'evaluation_strategy': 'steps',
+}
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+)
+
+trainer.train()