Create train.py

train.py

@@ -0,0 +1,36 @@
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
+
+# Load your dataset
+dataset = load_dataset('text', data_files={'train': 'cleaned_data.txt'})
+
+# Preprocess the dataset
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+def tokenize_function(examples):
+    return tokenizer(examples['text'], padding="max_length", truncation=True)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Load model
+model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=3,
+    weight_decay=0.01,
+)
+
+# Create Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["train"],
+)
+
+# Train the model
+trainer.train()