Rename testcode.py to train.py

testcode.py

@@ -1,3 +0,0 @@
-print("Hello Word")
-print("test")
-print("Hello Word")

train.py

@@ -0,0 +1,53 @@
+# File 1: Model Repo Code (train.py)
+# This file contains steps 1 to 4
+
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, TrainingArguments, Trainer
+
+# Step 1: Load the Dataset
+dataset = load_dataset("squad")
+
+# Step 2: Preprocess the Dataset
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+def preprocess_function(examples):
+    return tokenizer(
+        examples["question"],
+        examples["context"],
+        truncation=True,
+        max_length=384,
+        stride=128,
+        return_overflowing_tokens=True,
+        padding="max_length"
+    )
+
+tokenized_dataset = dataset.map(preprocess_function, batched=True)
+
+# Step 3: Train the Model
+model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased")
+
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=3e-5,
+    per_device_train_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    push_to_hub=True,  # Automatically push to the Hugging Face Hub
+    hub_model_id="username/qa_model_repo"  # Replace with your username and model repo name
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["validation"],
+)
+
+trainer.train()
+
+# Step 4: Push the Model and Tokenizer to Hugging Face Hub
+model.push_to_hub("username/qa_model_repo")
+tokenizer.push_to_hub("username/qa_model_repo")
+
+print("Model and tokenizer pushed to Hugging Face Hub successfully!")