Learn HUGGINGFACE-TRANSFORMERS with Real Code Examples

The library provides access to models like BERT, GPT, T5, RoBERTa, and many others for NLP, as well as Vision Transformers (ViT) and Wav2Vec for speech and vision.

It offers a unified interface for tokenization, model training, and inference.

Emphasizes ease-of-use, interoperability, and access to a growing hub of pre-trained models.

Pre-trained transformer architectures (BERT, GPT, T5, etc.)

AutoModel, AutoTokenizer, and AutoConfig for easy model loading

Pipelines for zero-shot and one-line inference

Trainer and Trainer API for training and evaluation

Support for quantization, pruning, and accelerated inference

Model: transformer architecture (BERT, GPT, etc.)

Tokenizer: converts text to token IDs

Pipeline: end-to-end processing for tasks

Trainer: training and evaluation utility

Pre-trained weights: learned parameters for transfer learning

main.py - model training or inference

data/ - datasets for NLP tasks

models/ - saved Hugging Face models

notebooks/ - experimentation and prototyping

utils/ - data preprocessing or helper scripts

Load a pre-trained model using AutoModel/AutoModelForSequenceClassification

Load the corresponding tokenizer using AutoTokenizer

Prepare datasets and tokenize text

Fine-tune the model using Trainer or custom loops

Perform inference using pipelines or model.forward()

Save and deploy models for production

Beginner: text classification or sentiment analysis

Intermediate: named entity recognition, summarization

Advanced: multi-task NLP or sequence-to-sequence modeling

Expert: custom transformer architectures or large-scale fine-tuning

Enterprise: production-ready model deployment and scaling

Transformers vs PyTorch: high-level pre-trained models vs general ML library

Transformers vs TensorFlow: model hub vs framework

Transformers vs spaCy: advanced transformer NLP vs traditional NLP pipelines

Transformers vs OpenAI GPT API: local models vs cloud API

Transformers vs FastAI: pre-trained transformers vs high-level ML wrappers

2016 – Hugging Face founded

2019 – Transformers library released

2020 – Added Trainer API, pipelines, and more pre-trained models

2021 – Integration with Datasets library and Accelerate

2025 – Latest version with extensive model hub and multi-modal support

Transformer: attention-based model architecture

Tokenizer: converts text to numerical inputs

Pipeline: end-to-end inference abstraction

Fine-tuning: adapting pre-trained models to new tasks

Model hub: collection of pre-trained models