Transformers have revolutionized NLP. Let’s explore how to get started with Hugging Face’s transformers library.

Why Transformers?

Before transformers, we relied on RNNs and LSTMs for sequence tasks. Transformers brought:

  • Parallel processing capabilities
  • Better long-range dependencies
  • Transfer learning revolution

Quick Start

from transformers import pipeline

# Sentiment analysis in 3 lines
classifier = pipeline("sentiment-analysis")
result = classifier("I love writing technical blogs!")
print(result)  # [{'label': 'POSITIVE', 'score': 0.999}]

Loading a Pre-trained Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Tokenize input
inputs = tokenizer("Hello, transformers!", return_tensors="pt")

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

Stay tuned for more advanced transformer tutorials!