Hugging Face

Hub for pretrained models and the Transformers library.

When to Use

• NLP tasks (classification, NER, QA)
• Using pretrained models
• Fine-tuning transformers
• Model hosting and inference

Quick Start

from transformers import pipeline

# Zero-shot classification
classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
result = classifier("This movie is fantastic!")
print(result)  # [{'label': 'POSITIVE', 'score': 0.99}]

Core Concepts

Pipelines

from transformers import pipeline

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

# Summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
summary = summarizer(long_text, max_length=130, min_length=30)

# Question answering
qa = pipeline("question-answering")
answer = qa(question="What is Python?", context="Python is a programming language...")

Model Loading

from transformers import AutoTokenizer, AutoModelForSequenceClassification

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

inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)

Common Patterns

Fine-tuning with Trainer

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=64,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir="./logs",
    evaluation_strategy="epoch"
)

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

trainer.train()

Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/gpt2"
headers = {"Authorization": f"Bearer {HF_TOKEN}"}

response = requests.post(API_URL, headers=headers, json={"inputs": "Hello"})

Best Practices

Do:

• Use Auto classes for flexibility
• Cache models locally
• Use device_map="auto" for large models
• Quantize for deployment

Don’t:

• Load full model if using pipeline
• Ignore tokenizer special tokens
• Skip input validation
• Fine-tune without freezing layers

Troubleshooting

References

• Hugging Face Docs
• Model Hub