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
python
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
python
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
python
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
python
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
python
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
| Issue | Cause | Solution |
|---|---|---|
| OOM error | Model too large | Use quantization or smaller model |
| Slow inference | No GPU | Enable CUDA or use quantized |
| Token mismatch | Wrong tokenizer | Use matching tokenizer |