Hugging Face Intro

# Description

Introductory project to Hugging Face Transformers library for natural language processing. Covers core concepts: loading pre-trained models from Hugging Face Hub, tokenization with AutoTokenizer, the transformers pipeline for common tasks (text classification, sentiment analysis, machine translation, text summarization, question answering, mask filling) and the Hub API for discovering and sharing models. Includes practical examples with BERT, GPT-2, T5 and DistilBERT, comparing performance across NLP tasks.

$ ./deploy.sh $ git clone

# Key features

$ Transformers pipeline: sentiment, translation, summary, QA

$ Model loading from Hugging Face Hub with AutoModel

$ Tokenization with AutoTokenizer and padding/truncation config

$ Models: BERT, GPT-2, T5, DistilBERT and more

$ Hugging Face Hub API for model discovery

$ Practical NLP examples in few lines of code

# Gallery

Desktop view

Mobile view

# Technologies used

Python TensorFlow / Keras