Transformers

Overview Hugging Face Transformers is the most popular library for working with pre-trained language models. This guide covers installation, basic usage, and common NLP tasks. Installation pip install transformers torch Loading a Pre-trained Model from transformers import pipeline # Text classification classifier = pipeline("sentiment-analysis") result = classifier("I love using Hugging Face!") print(result) # [{'label': 'POSITIVE', 'score': 0.9998}] Text Generation generator = pipeline("text-generation", model="gpt2") output = generator("The future of AI is", max_length=50) print(output[0](#)) Named Entity Recognition ner = pipeline("ner", grouped_entities=True) text = "Apple was founded by Steve Jobs in Cupertino." entities = ner(text) # [{'entity_group': 'ORG', 'word': 'Apple'}, ...] Fine-tuning a Model from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2) training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=16, evaluation_strategy="epoch" ) trainer = Trainer(model=model, args=training_args, train_dataset=train_data) trainer.train() Key Resources Hugging Face Documentation Model Hub - 400,000+ pre-trained models Datasets Library