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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainer, TrainingArguments
from datasets import load_dataset
# Define model and tokenizer names
model_name = "facebook/bart-base"
tokenizer_name = model_name
# Load dataset
dataset = load_dataset("cnn_dailymail", split="train")
# Preprocess data (example) - define your cleaning and tokenization functions here
def preprocess_function(examples):
inputs = [ex["article"] for ex in examples]
targets = [ex["highlights"] for ex in examples]
# Tokenize inputs and targets, add padding
tokenized_data = tokenizer(inputs, targets, padding="max_length", truncation=True)
return tokenized_data
# Preprocess train and validation data
train_data = dataset.map(preprocess_function, batched=True)
# Define training arguments
training_args = TrainingArguments(
output_dir="./outputs", # any desired output directory
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3, # Adjust number of epochs for training
save_steps=10_000,
evaluation_strategy="epoch",
logging_steps=500,
push_to_hub=True, # Set to True for direct upload to Hub during training
)
# Load pre-trained model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
# Define Trainer instance
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=train_data,
tokenizer=tokenizer,
)
# Start training
trainer.train()
# Model is now trained and uploaded to the Hub if push_to_hub was True
# For manual upload after training, we use the Hub API (refer to Hugging Face documentation) |