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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()
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