Learn HUGGINGFACE-TRANSFORMERS with Real Code Examples

Sentiment analysis on IMDB reviews

Named entity recognition with CoNLL dataset

Text summarization with BART or T5

Machine translation with MarianMT

Question answering with BERT or RoBERTa

Ensure model and tokenizer versions match

Handle tokenization errors with padding/truncation

Check GPU memory for large models

Fix shape mismatches for batch inputs

Resolve framework compatibility (PyTorch vs TensorFlow)

Unit-test preprocessing and tokenization

Validate output shapes and logits

Check for correct mapping of labels

Monitor GPU utilization and performance

Evaluate on validation/test datasets

Hugging Face Inference API

Transformers pipeline for real-time inference

ONNX/TensorRT optimized models

Cloud deployment (AWS, GCP, Azure)

Containerized deployment with Docker

Datasets library for standardized datasets

Tokenizers library for fast tokenization

Accelerate for distributed and mixed-precision training

Hugging Face Hub for model sharing and downloading

Optimum for hardware-optimized model inference

PyTorch, TensorFlow, JAX backends

Datasets library for training/evaluation

Integration with MLflow or Weights & Biases for tracking

ONNX and TensorRT for optimized deployment

Integration with Gradio or Streamlit for demos

Use pipelines for rapid prototyping

Leverage pre-trained models to save time

Use Accelerate for distributed training

Batch inputs for efficient inference

Fine-tune smaller models first before scaling

Fine-tune BERT for sentiment analysis

Use T5 for text summarization

Implement zero-shot classification with pipelines

Optimize large model inference

Deploy a transformer model to a cloud API