Welcome to the Hugging Face tutorial! This guide will walk you through the basics of using Hugging Face's powerful NLP tools and models. Whether you're a beginner or an experienced developer, you'll find this tutorial helpful in getting started with Hugging Face.

What is Hugging Face?

Hugging Face is an open-source community platform that provides a vast collection of pre-trained models and tools for natural language processing (NLP). It's a one-stop shop for all your NLP needs, from text classification to machine translation.

Getting Started

Before you dive into the tutorial, make sure you have the following prerequisites:

  • Python 3.6 or later
  • pip (Python package installer)
  • An internet connection

Install Hugging Face Transformers

To get started, you'll need to install the Hugging Face Transformers library. You can do this by running the following command in your terminal:

pip install transformers

Quick Start Guide

Here's a quick overview of the steps you'll follow in this tutorial:

  1. Import the library: Import the Transformers library in your Python script.
  2. Load a model: Load a pre-trained model from the Hugging Face model hub.
  3. Use the model: Use the loaded model to perform NLP tasks, such as text classification or language detection.
  4. Customize and train your own model: If you want to go further, you can customize and train your own model using the Hugging Face platform.

Example: Text Classification

Let's start with a simple text classification example. We'll use the DistilBertForSequenceClassification model to classify text into two categories: "Positive" and "Negative".

from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

# Load the tokenizer and model
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')

# Encode the text
text = "I love Hugging Face!"
encoded_input = tokenizer(text, return_tensors='pt')

# Predict the category
predictions = model(**encoded_input)
print(predictions)

Further Reading

For more detailed information and tutorials, check out the following resources:

If you have any questions or need further assistance, feel free to reach out to the Hugging Face community on Stack Overflow.

🎉 Ready to dive deeper into the world of NLP with Hugging Face? Check out our comprehensive tutorials and guides on Hugging Face Tutorials! 🌟