Hugging Face Tutorial for Beginners

Install and explore the transformers library from hugging face to access thousands of pre-trained models and pipelines. Apply it to NLP tasks like translation, summarization, classification, and question answering.

Discover how the Hugging Face datasets library provides easy access to thousands of NLP datasets with a unified API, lazy loading, and simple pip, Colab, or GitHub installation.

Explore the Hugging Face tokenizers library, a fast, flexible tool for tokenizing text, its integration with transformers, and installation options supporting spam detection, sentiment analysis, translation, and more.

Discover what a Hugging Face access token is and how to create an API key, including when you need it for private models, inference APIs, or uploads.

download a dataset from Hugging Face using the datasets library, load the IMDb dataset for sentiment classification, and view train and test splits in a Colab notebook.

Learn how to download a model from Hugging Face with the transformers library, using from_pretrained, and understand weights, configuration, tokenizer, and the 768-dimensional BERT base uncased token representations.

Learn to implement sentiment analysis with Hugging Face and the Transformers library, covering polarity, emotion, aspect-based, and intent analysis through a Google Colab coding demo.

Learn to perform text classification with Hugging Face, compare it to sentiment analysis, and build a spam detection example in Google Colab using a pre-trained model.

Explore text summarization with Hugging Face using the Transformers library on Google Colab. Load a sequence-to-sequence model and tokenizer, tokenize input, and generate summaries with 512 tokens and four-beam search.

Learn how to perform translation with Hugging Face transformers using text-to-text generation, including task prefixes, a T5-small model, and an end-to-end example translating English to Spanish.

Explore question answering with the hugging face transformers library, running Google Colab example that loads a pre-trained question answering model and tokenizer, tokenizes context and question, and decodes the answer.

Learn text-to-image generation with hugging face using the diffusers library and stable diffusion. Install libraries, load the pipeline in Google Colab, and generate images from text prompts.

Explore text to video synthesis with Hugging Face by generating frames from text, stitching them into a video with the diffusers library and OpenCV in Colab.