Create app.py

app.py

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+from datasets import load_dataset
+from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer, AutoTokenizer
+import torch
+
+# Load Dataset
+dataset = load_dataset("yelp_review_full")  # Example dataset
+
+# Load Pretrained Model & Tokenizer
+model_name = "bert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=5)
+
+# Tokenize Dataset
+def preprocess_function(examples):
+    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
+
+encoded_dataset = dataset.map(preprocess_function, batched=True)
+
+# Training Arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    push_to_hub=True  # Push trained model back to Hugging Face
+)
+
+# Define Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=encoded_dataset["train"],
+    eval_dataset=encoded_dataset["test"],
+    tokenizer=tokenizer
+)
+
+# Train the Model
+trainer.train()
+
+# Save & Push to Hub
+trainer.push_to_hub()