Learn HUGGINGFACE-TRANSFORMERS with Real Code Examples

Transformer encoder/decoder blocks

Self-attention and multi-head attention mechanisms

Pre-trained embeddings and positional encodings

Feed-forward layers and layer normalization

Configurable model heads for classification, generation, or token-level tasks

Transformer encoder/decoder blocks with attention

Tokenization of input text

Embedding lookup and positional encodings

Forward pass through model layers

Task-specific heads for predictions

Transformer-based architectures

Tokenization and preprocessing pipeline

Trainer API for training and evaluation

Pipeline abstraction for end-to-end inference

Hub integration for pre-trained models

BERT for classification and QA

GPT for text generation

T5/BART for summarization and translation

Vision Transformers for image classification

Wav2Vec for speech recognition

Unified API across frameworks

Pre-trained models for rapid prototyping

Extensible and modular architecture

Ease-of-use for inference and fine-tuning

Community-driven development and model sharing

Use Accelerate for distributed training

Enable mixed-precision to save memory

Batch inputs efficiently

Deploy via Hugging Face Inference API for scale

Profile models for performance optimization

Upgrade transformers library via pip/conda

Replace deprecated API calls

Check tokenizer/model version compatibility

Validate saved models on new version

Test pipelines after migration