FIRST COMMIT

launch_training.py

@@ -0,0 +1,45 @@
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Trainer, TrainingArguments
+
+# Load dataset
+dataset = load_dataset("Abdelkareem/wikihow-arabic-summarization")
+
+# Load the model and tokenizer
+model_name = "UBC-NLP/AraT5v2-base-1024"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Preprocessing function to tokenize the dataset
+def preprocess_function(examples):
+    inputs = examples["article"]
+    targets = examples["summarize"]
+    model_inputs = tokenizer(inputs, max_length=1024, truncation=True)
+    labels = tokenizer(targets, max_length=150, truncation=True)
+    model_inputs["labels"] = labels["input_ids"]
+    return model_inputs
+
+# Apply preprocessing to the dataset
+tokenized_datasets = dataset.map(preprocess_function, batched=True)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./results", 
+    evaluation_strategy="epoch", 
+    learning_rate=2e-5, 
+    per_device_train_batch_size=4, 
+    per_device_eval_batch_size=4, 
+    num_train_epochs=3,
+    weight_decay=0.01,
+    logging_dir="./logs"
+)
+
+# Initialize the Trainer
+trainer = Trainer(
+    model=model, 
+    args=training_args, 
+    train_dataset=tokenized_datasets["train"], 
+    eval_dataset=tokenized_datasets["validation"]
+)
+
+# Start the training process
+trainer.train()