Hugging Face Transformers Examples 📚

Welcome to the Hugging Face Transformers Examples page! Here, you'll find practical use cases and code snippets to get started with the Transformers library. Whether you're into NLP, computer vision, or other AI domains, these examples will help you explore the power of pre-trained models.

🌟 Key Examples

Text Classification

Use models like bert-base-uncased for sentiment analysis or topic categorization.
Example code:

from transformers import pipeline
classifier = pipeline("text-classification", model="bert-base-uncased")
print(classifier("I love programming!"))

🔄 Sequence-to-Sequence Tasks

Try t5-small for tasks like translation or summarization.
Example code:

from transformers import pipeline
translator = pipeline("translation", model="t5-small")
print(translator("Hello, world!", src_lang="en", tgt_lang="fr"))

📊 Custom Training

Fine-tune models on your dataset using Trainer API.
Example code:

from transformers import Trainer, TrainingArguments
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    tokenizer=tokenizer,
    callbacks=[EarlyStoppingCallback(early_stopping_patience=2)]
)

🧠 Why Use Transformers?

🚀 Pre-trained models save time and computational resources
📁 Easy integration with Hugging Face Hub
🔄 Simple API for common NLP tasks
🌍 Community-driven model repository

For more detailed guides, check out our official documentation on the Transformers library.

📚 Recommended Reading