Core Libraries

Using Pre-trained Models

from transformers import pipeline

# Text generation
generator = pipeline('text-generation', model='gpt2')
result = generator("Once upon a time", max_length=50)

# Sentiment analysis
classifier = pipeline('sentiment-analysis')
result = classifier("I love this product!")

# Question answering
qa = pipeline('question-answering')
result = qa(question="What is AI?", context="AI stands for...")

# Translation
translator = pipeline('translation_en_to_fr')
result = translator("Hello, how are you?")

Fine-tuning

from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer

model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)

trainer.train()

Best Practices

• Use pipelines for quick prototyping
• Fine-tune on your specific task
• Use datasets library for data loading
• Share models on Hugging Face Hub