import torch from transformers import BartForConditionalGeneration, BartTokenizer, Trainer, TrainingArguments # Define your dataset and dataloader (not provided here, as it depends on your data format) # Load the BART model and tokenizer model_name = "facebook/bart-large-cnn" # You can choose a different model tokenizer = BartTokenizer.from_pretrained(model_name) model = BartForConditionalGeneration.from_pretrained(model_name) # Set up training arguments training_args = TrainingArguments( output_dir="./output", num_train_epochs=3, # Adjust as needed per_device_train_batch_size=4, # Adjust as needed save_steps=10_000, # Save the model checkpoint after a certain number of steps logging_steps=100, # Log training progress every N steps evaluation_strategy="steps", # Evaluate every N steps eval_steps=1000, # Evaluate every N steps save_total_limit=5, # Limit the number of saved checkpoints ) # Initialize Trainer trainer =
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