Hugging Face Transformers – State-of-the-Art NLP Library

Hugging Face Transformers is the most popular library for working with pre-trained transformer models. It provides thousands of ready-to-use models for natural language processing, computer vision, audio, and multimodal tasks. With simple APIs and seamless integration with PyTorch and TensorFlow, Transformers has become the go-to library for both research and production AI applications.

Key Features

• Model Hub – Access 200,000+ pre-trained models
• Pipeline API – One-line inference for common tasks
• Multi-Framework – Works with PyTorch, TensorFlow, and JAX
• Fine-Tuning – Easy model customization with Trainer API
• Tokenizers – Fast, efficient text tokenization
• Datasets – Companion library for data loading
• Accelerate – Distributed training made simple

Installation

# Install with pip
pip install transformers

# With PyTorch backend
pip install transformers[torch]

# With TensorFlow backend
pip install transformers[tf-cpu]

# Install all optional dependencies
pip install transformers[all]

Quick Start with Pipelines

from transformers import pipeline

# Sentiment analysis
classifier = pipeline("sentiment-analysis")
result = classifier("I love using Linux!")
# [{'label': 'POSITIVE', 'score': 0.9998}]

# Text generation
generator = pipeline("text-generation", model="gpt2")
text = generator("Linux is", max_length=50)

# Question answering
qa = pipeline("question-answering")
answer = qa(question="What is Linux?", context="Linux is an open-source operating system kernel.")

# Image classification
classifier = pipeline("image-classification")
result = classifier("image.jpg")

# Speech recognition
transcriber = pipeline("automatic-speech-recognition")
text = transcriber("audio.wav")

Popular Models

• BERT – Bidirectional encoder for text understanding
• GPT-2/GPT-Neo – Text generation models
• Llama 2 – Meta’s powerful open LLM
• Mistral – Efficient high-performance model
• Whisper – OpenAI’s speech recognition
• CLIP – Vision-language understanding
• Stable Diffusion – Image generation

Fine-Tuning Example

from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset

# Load model and dataset
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
dataset = load_dataset("imdb")

# Configure training
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16,
)

# Train
trainer = Trainer(model=model, args=training_args, train_dataset=dataset["train"])
trainer.train()

Use Cases

• Chatbots – Build conversational AI systems
• Content Analysis – Sentiment, classification, summarization
• Translation – Multi-language text translation
• Code Generation – AI-assisted programming
• Image Analysis – Classification, detection, captioning

Download Transformers